paper_id stringlengths 15 35 | title stringlengths 26 182 | authors listlengths 1 25 | isca_url stringlengths 66 86 | pdf_url stringlengths 65 85 | doi stringlengths 27 30 | pages stringlengths 3 9 | bibtex large_stringlengths 294 850 | abstract large_stringlengths 247 1.59k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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|---|---|---|---|---|---|---|---|---|---|---|
chanclu21_interspeech | Automatic Classification of Phonation Types in Spontaneous Speech: Towards a New Workflow for the Characterization of Speakers’ Voice Quality | [
"Anaïs Chanclu",
"Imen Ben Amor",
"Cédric Gendrot",
"Emmanuel Ferragne",
"Jean-François Bonastre"
] | https://www.isca-archive.org/interspeech_2021/chanclu21_interspeech.html | https://www.isca-archive.org/interspeech_2021/chanclu21_interspeech.pdf | 10.21437/Interspeech.2021-1765 | 1015-1018 | @inproceedings{chanclu21_interspeech,
title = {{Automatic Classification of Phonation Types in Spontaneous Speech: Towards a New Workflow for the Characterization of Speakers’ Voice Quality}},
author = {Anaïs Chanclu and Imen Ben Amor and Cédric Gendrot and Emmanuel Ferragne and Jean-François Bonastre},
ye... | Voice quality is known to be an important factor for the characterization
of a speaker’s voice, both in terms of physiological features
(mainly laryngeal and supralaryngeal) and of the speaker’s habits
(sociolinguistic factors). This paper is devoted to one of the main
components of voice quality: phonation type. It pr... | null | null |
son21_interspeech | Measuring Voice Quality Parameters After Speaker Pseudonymization | [
"Rob J.J.H. van Son"
] | https://www.isca-archive.org/interspeech_2021/son21_interspeech.html | https://www.isca-archive.org/interspeech_2021/son21_interspeech.pdf | 10.21437/Interspeech.2021-26 | 1019-1023 | @inproceedings{son21_interspeech,
title = {{Measuring Voice Quality Parameters After Speaker Pseudonymization}},
author = {Rob J.J.H. van Son},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1019--1023},
doi = {10.21437/Interspeech.2021-26},
issn = {2958-1796},
} | Collecting and sharing speech resources is important for progress in
speech science and technology. Often, speech resources cannot be shared
because of concerns over the privacy of the speakers, e.g., minors
or people with medical conditions. Current technologies for pseudonymizing
speech have only been tested on “stan... | null | null |
steinert21_interspeech | Audio-Visual Recognition of Emotional Engagement of People with Dementia | [
"Lars Steinert",
"Felix Putze",
"Dennis Küster",
"Tanja Schultz"
] | https://www.isca-archive.org/interspeech_2021/steinert21_interspeech.html | https://www.isca-archive.org/interspeech_2021/steinert21_interspeech.pdf | 10.21437/Interspeech.2021-567 | 1024-1028 | @inproceedings{steinert21_interspeech,
title = {{Audio-Visual Recognition of Emotional Engagement of People with Dementia}},
author = {Lars Steinert and Felix Putze and Dennis Küster and Tanja Schultz},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1024--1028},
doi = {10.21... | Dementia places an immeasurable burden on affected individuals and
caregivers. In addition to general cognitive decline, dementia has
a negative impact on communication. Technical activation systems are
thus in high demand, as cognitive activation may help to moderate the
decline. However, effective activation requires... | null | null |
hecker21_interspeech | Speaking Corona? Human and Machine Recognition of COVID-19 from Voice | [
"Pascal Hecker",
"Florian B. Pokorny",
"Katrin D. Bartl-Pokorny",
"Uwe Reichel",
"Zhao Ren",
"Simone Hantke",
"Florian Eyben",
"Dagmar M. Schuller",
"Bert Arnrich",
"Björn W. Schuller"
] | https://www.isca-archive.org/interspeech_2021/hecker21_interspeech.html | https://www.isca-archive.org/interspeech_2021/hecker21_interspeech.pdf | 10.21437/Interspeech.2021-1771 | 1029-1033 | @inproceedings{hecker21_interspeech,
title = {{Speaking Corona? Human and Machine Recognition of COVID-19 from Voice}},
author = {Pascal Hecker and Florian B. Pokorny and Katrin D. Bartl-Pokorny and Uwe Reichel and Zhao Ren and Simone Hantke and Florian Eyben and Dagmar M. Schuller and Bert Arnrich and Björn... | With the COVID-19 pandemic, several research teams have reported successful
advances in automated recognition of COVID-19 by voice. Resulting voice-based
screening tools for COVID-19 could support large-scale testing efforts.
While capabilities of machines on this task are progressing, we approach
the so far unexplored... | null | null |
nguyen21b_interspeech | Acoustic-Prosodic, Lexical and Demographic Cues to Persuasiveness in Competitive Debate Speeches | [
"Huyen Nguyen",
"Ralph Vente",
"David Lupea",
"Sarah Ita Levitan",
"Julia Hirschberg"
] | https://www.isca-archive.org/interspeech_2021/nguyen21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/nguyen21b_interspeech.pdf | 10.21437/Interspeech.2021-1891 | 1034-1038 | @inproceedings{nguyen21b_interspeech,
title = {{Acoustic-Prosodic, Lexical and Demographic Cues to Persuasiveness in Competitive Debate Speeches}},
author = {Huyen Nguyen and Ralph Vente and David Lupea and Sarah Ita Levitan and Julia Hirschberg},
year = {2021},
booktitle = {{Interspeech 2021}},
p... | We analyze the acoustic-prosodic and lexical correlates of persuasiveness,
taking into account speaker, judge and debate characteristics in a
novel data set of 674 audio profiles, transcripts, evaluation scores
and demographic data from professional debate tournament speeches.
By conducting 10-fold cross validation exp... | null | null |
borgstrom21_interspeech | Unsupervised Bayesian Adaptation of PLDA for Speaker Verification | [
"Bengt J. Borgström"
] | https://www.isca-archive.org/interspeech_2021/borgstrom21_interspeech.html | https://www.isca-archive.org/interspeech_2021/borgstrom21_interspeech.pdf | 10.21437/Interspeech.2021-33 | 1039-1043 | @inproceedings{borgstrom21_interspeech,
title = {{Unsupervised Bayesian Adaptation of PLDA for Speaker Verification}},
author = {Bengt J. Borgström},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1039--1043},
doi = {10.21437/Interspeech.2021-33},
issn = {2958-1796},
... | This paper presents a Bayesian framework for unsupervised domain adaptation
of Probabilistic Linear Discriminant Analysis (PLDA). By interpreting
class labels as latent random variables, Variational Bayes (VB) is
used to derive a maximum a posterior (MAP) solution of the adapted
PLDA model when labels are missing, refe... | null | null |
wang21i_interspeech | The DKU-Duke-Lenovo System Description for the Fearless Steps Challenge Phase III | [
"Weiqing Wang",
"Danwei Cai",
"Jin Wang",
"Qingjian Lin",
"Xuyang Wang",
"Mi Hong",
"Ming Li"
] | https://www.isca-archive.org/interspeech_2021/wang21i_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21i_interspeech.pdf | 10.21437/Interspeech.2021-235 | 1044-1048 | @inproceedings{wang21i_interspeech,
title = {{The DKU-Duke-Lenovo System Description for the Fearless Steps Challenge Phase III}},
author = {Weiqing Wang and Danwei Cai and Jin Wang and Qingjian Lin and Xuyang Wang and Mi Hong and Ming Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | This paper describes the systems developed by the DKU-Duke-Lenovo team
for the Fearless Steps Challenge Phase III. For the speech activity
detection (SAD) task, we employ the U-Net-based model which has not
been used for SAD before, observing a DCF of 1.915% on the eval set.
For the speaker identification (SID) task, w... | null | null |
chen21f_interspeech | Improved Meta-Learning Training for Speaker Verification | [
"Yafeng Chen",
"Wu Guo",
"Bin Gu"
] | https://www.isca-archive.org/interspeech_2021/chen21f_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21f_interspeech.pdf | 10.21437/Interspeech.2021-405 | 1049-1053 | @inproceedings{chen21f_interspeech,
title = {{Improved Meta-Learning Training for Speaker Verification}},
author = {Yafeng Chen and Wu Guo and Bin Gu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1049--1053},
doi = {10.21437/Interspeech.2021-405},
issn = {2958-1796... | Meta-learning (ML) has recently become a research hotspot in speaker
verification (SV). We introduce two methods to improve the meta-learning
training for SV in this paper. For the first method, a backbone embedding
network is first jointly trained with the conventional cross entropy
loss and prototypical networks (PN)... | 2103.15421 | title_snapshot |
wang21j_interspeech | Variational Information Bottleneck Based Regularization for Speaker Recognition | [
"Dan Wang",
"Yuanjie Dong",
"Yaxing Li",
"Yunfei Zi",
"Zhihui Zhang",
"Xiaoqi Li",
"Shengwu Xiong"
] | https://www.isca-archive.org/interspeech_2021/wang21j_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21j_interspeech.pdf | 10.21437/Interspeech.2021-482 | 1054-1058 | @inproceedings{wang21j_interspeech,
title = {{Variational Information Bottleneck Based Regularization for Speaker Recognition}},
author = {Dan Wang and Yuanjie Dong and Yaxing Li and Yunfei Zi and Zhihui Zhang and Xiaoqi Li and Shengwu Xiong},
year = {2021},
booktitle = {{Interspeech 2021}},
pages... | Speaker recognition (SR) is inevitably affected by noise in real-life
scenarios, resulting in decreased recognition accuracy. In this paper,
we introduce a novel regularization method, variable information bottleneck
(VIB), in speaker recognition to extract robust speaker embeddings.
VIB prompts the neural network to i... | null | null |
brummer21_interspeech | Out of a Hundred Trials, How Many Errors Does Your Speaker Verifier Make? | [
"Niko Brümmer",
"Luciana Ferrer",
"Albert Swart"
] | https://www.isca-archive.org/interspeech_2021/brummer21_interspeech.html | https://www.isca-archive.org/interspeech_2021/brummer21_interspeech.pdf | 10.21437/Interspeech.2021-541 | 1059-1063 | @inproceedings{brummer21_interspeech,
title = {{Out of a Hundred Trials, How Many Errors Does Your Speaker Verifier Make?}},
author = {Niko Brümmer and Luciana Ferrer and Albert Swart},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1059--1063},
doi = {10.21437/Interspeech.2... | Out of a hundred trials, how many errors does your speaker verifier
make? For the user this is an important, practical question, but researchers
and vendors typically sidestep it and supply instead the conditional
error-rates that are given by the ROC/DET curve. We posit that the
user’s question is answered by the Baye... | 2104.00732 | title_snapshot |
chojnacka21_interspeech | SpeakerStew: Scaling to Many Languages with a Triaged Multilingual Text-Dependent and Text-Independent Speaker Verification System | [
"Roza Chojnacka",
"Jason Pelecanos",
"Quan Wang",
"Ignacio Lopez Moreno"
] | https://www.isca-archive.org/interspeech_2021/chojnacka21_interspeech.html | https://www.isca-archive.org/interspeech_2021/chojnacka21_interspeech.pdf | 10.21437/Interspeech.2021-646 | 1064-1068 | @inproceedings{chojnacka21_interspeech,
title = {{SpeakerStew: Scaling to Many Languages with a Triaged Multilingual Text-Dependent and Text-Independent Speaker Verification System}},
author = {Roza Chojnacka and Jason Pelecanos and Quan Wang and Ignacio Lopez Moreno},
year = {2021},
booktitle = {{I... | In this paper, we describe SpeakerStew — a hybrid system
to perform speaker verification on 46 languages. Two core ideas were
explored in this system: (1) Pooling training data of different languages
together for multilingual generalization and reducing development cycles;
(2) A novel triage mechanism between text-depe... | 2104.02125 | title_snapshot |
wang21k_interspeech | AntVoice Neural Speaker Embedding System for FFSVC 2020 | [
"Zhiming Wang",
"Furong Xu",
"Kaisheng Yao",
"Yuan Cheng",
"Tao Xiong",
"Huijia Zhu"
] | https://www.isca-archive.org/interspeech_2021/wang21k_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21k_interspeech.pdf | 10.21437/Interspeech.2021-966 | 1069-1073 | @inproceedings{wang21k_interspeech,
title = {{AntVoice Neural Speaker Embedding System for FFSVC 2020}},
author = {Zhiming Wang and Furong Xu and Kaisheng Yao and Yuan Cheng and Tao Xiong and Huijia Zhu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1069--1073},
doi = {10.... | This paper presents a comprehensive description of the AntVoice system
for the first two tracks of far-field speaker verification from single
microphone array in FFSVC 2020 [1]. The system is based on neural speaker
embeddings from deep neural network-based encoder networks. These encoder
networks for acoustic modeling... | null | null |
li21b_interspeech | Gradient Regularization for Noise-Robust Speaker Verification | [
"Jianchen Li",
"Jiqing Han",
"Hongwei Song"
] | https://www.isca-archive.org/interspeech_2021/li21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21b_interspeech.pdf | 10.21437/Interspeech.2021-1216 | 1074-1078 | @inproceedings{li21b_interspeech,
title = {{Gradient Regularization for Noise-Robust Speaker Verification}},
author = {Jianchen Li and Jiqing Han and Hongwei Song},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1074--1078},
doi = {10.21437/Interspeech.2021-1216},
issn ... | Noise robustness is a challenge for speaker recognition systems. To
solve this problem, one of the most common approaches is to joint-train
a model by using both clean and noisy utterances. However, the gradients
calculated on noisy utterances generally contain speaker-irrelevant
noisy components, resulting in overfitt... | null | null |
kataria21_interspeech | Deep Feature CycleGANs: Speaker Identity Preserving Non-Parallel Microphone-Telephone Domain Adaptation for Speaker Verification | [
"Saurabh Kataria",
"Jesús Villalba",
"Piotr Żelasko",
"Laureano Moro-Velázquez",
"Najim Dehak"
] | https://www.isca-archive.org/interspeech_2021/kataria21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kataria21_interspeech.pdf | 10.21437/Interspeech.2021-1502 | 1079-1083 | @inproceedings{kataria21_interspeech,
title = {{Deep Feature CycleGANs: Speaker Identity Preserving Non-Parallel Microphone-Telephone Domain Adaptation for Speaker Verification}},
author = {Saurabh Kataria and Jesús Villalba and Piotr Żelasko and Laureano Moro-Velázquez and Najim Dehak},
year = {2021}... | With the increase in the availability of speech from varied domains,
it is imperative to use such out-of-domain data to improve existing
speech systems. Domain adaptation is a prominent pre-processing approach
for this. We investigate it to adapt microphone speech to the telephone
domain. Specifically, we explore Cycle... | 2104.01433 | title_snapshot |
pu21_interspeech | Scaling Effect of Self-Supervised Speech Models | [
"Jie Pu",
"Yuguang Yang",
"Ruirui Li",
"Oguz Elibol",
"Jasha Droppo"
] | https://www.isca-archive.org/interspeech_2021/pu21_interspeech.html | https://www.isca-archive.org/interspeech_2021/pu21_interspeech.pdf | 10.21437/Interspeech.2021-1935 | 1084-1088 | @inproceedings{pu21_interspeech,
title = {{Scaling Effect of Self-Supervised Speech Models}},
author = {Jie Pu and Yuguang Yang and Ruirui Li and Oguz Elibol and Jasha Droppo},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1084--1088},
doi = {10.21437/Interspeech.2021-1935}... | The success of modern deep learning systems is built on two cornerstones,
massive amount of annotated training data and advanced computational
infrastructure to support large-scale computation. In recent years,
the model size of state-of-the-art deep learning systems has rapidly
increased and sometimes reached to billi... | null | null |
wu21c_interspeech | Joint Feature Enhancement and Speaker Recognition with Multi-Objective Task-Oriented Network | [
"Yibo Wu",
"Longbiao Wang",
"Kong Aik Lee",
"Meng Liu",
"Jianwu Dang"
] | https://www.isca-archive.org/interspeech_2021/wu21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/wu21c_interspeech.pdf | 10.21437/Interspeech.2021-1978 | 1089-1093 | @inproceedings{wu21c_interspeech,
title = {{Joint Feature Enhancement and Speaker Recognition with Multi-Objective Task-Oriented Network}},
author = {Yibo Wu and Longbiao Wang and Kong Aik Lee and Meng Liu and Jianwu Dang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1089--1093},... | Recently, increasing attention has been paid to the joint training
of upstream and downstream tasks, and to address the challenge of how
to synchronize various loss functions in a multi-objective scenario.
In this paper, to address the competing gradient directions between
the speaker classification loss and the featur... | null | null |
zhang21g_interspeech | Multi-Level Transfer Learning from Near-Field to Far-Field Speaker Verification | [
"Li Zhang",
"Qing Wang",
"Kong Aik Lee",
"Lei Xie",
"Haizhou Li"
] | https://www.isca-archive.org/interspeech_2021/zhang21g_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21g_interspeech.pdf | 10.21437/Interspeech.2021-1980 | 1094-1098 | @inproceedings{zhang21g_interspeech,
title = {{Multi-Level Transfer Learning from Near-Field to Far-Field Speaker Verification}},
author = {Li Zhang and Qing Wang and Kong Aik Lee and Lei Xie and Haizhou Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1094--1098},
doi = ... | In far-field speaker verification, the performance of speaker embeddings
is susceptible to degradation when there is a mismatch between the
conditions of enrollment and test speech. To solve this problem, we
propose the feature-level and instance-level transfer learning in the
teacher-student framework to learn a domai... | 2106.09320 | title_snapshot |
patino21_interspeech | Speaker Anonymisation Using the McAdams Coefficient | [
"Jose Patino",
"Natalia Tomashenko",
"Massimiliano Todisco",
"Andreas Nautsch",
"Nicholas Evans"
] | https://www.isca-archive.org/interspeech_2021/patino21_interspeech.html | https://www.isca-archive.org/interspeech_2021/patino21_interspeech.pdf | 10.21437/Interspeech.2021-1070 | 1099-1103 | @inproceedings{patino21_interspeech,
title = {{Speaker Anonymisation Using the McAdams Coefficient}},
author = {Jose Patino and Natalia Tomashenko and Massimiliano Todisco and Andreas Nautsch and Nicholas Evans},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1099--1103},
doi ... | Anonymisation has the goal of manipulating speech signals in order
to degrade the reliability of automatic approaches to speaker recognition,
while preserving other aspects of speech, such as those relating to
intelligibility and naturalness. This paper reports an approach to
anonymisation that, unlike other current ap... | 2011.01130 | title_snapshot |
luo21_interspeech | Multi-Stream Gated and Pyramidal Temporal Convolutional Neural Networks for Audio-Visual Speech Separation in Multi-Talker Environments | [
"Yiyu Luo",
"Jing Wang",
"Liang Xu",
"Lidong Yang"
] | https://www.isca-archive.org/interspeech_2021/luo21_interspeech.html | https://www.isca-archive.org/interspeech_2021/luo21_interspeech.pdf | 10.21437/Interspeech.2021-366 | 1104-1108 | @inproceedings{luo21_interspeech,
title = {{Multi-Stream Gated and Pyramidal Temporal Convolutional Neural Networks for Audio-Visual Speech Separation in Multi-Talker Environments}},
author = {Yiyu Luo and Jing Wang and Liang Xu and Lidong Yang},
year = {2021},
booktitle = {{Interspeech 2021}},
pa... | Speech separation is the task of extracting target speech from noisy
mixture. In applications like video telephones or video conferencing,
lip movements of the target speaker are accessible, which can be leveraged
for speech separation. This paper proposes a time-domain audio-visual
speech separation model under multi-... | null | null |
wang21l_interspeech | TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation | [
"Helin Wang",
"Bo Wu",
"Lianwu Chen",
"Meng Yu",
"Jianwei Yu",
"Yong Xu",
"Shi-Xiong Zhang",
"Chao Weng",
"Dan Su",
"Dong Yu"
] | https://www.isca-archive.org/interspeech_2021/wang21l_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21l_interspeech.pdf | 10.21437/Interspeech.2021-481 | 1109-1113 | @inproceedings{wang21l_interspeech,
title = {{TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation}},
author = {Helin Wang and Bo Wu and Lianwu Chen and Meng Yu and Jianwei Yu and Yong Xu and Shi-Xiong Zhang and Chao Weng and Dan Su and Dong Yu},
year = {2021},
... | In this paper, we exploit the effective way to leverage contextual
information to improve the speech dereverberation performance in real-world
reverberant environments. We propose a temporal-contextual attention
approach on the deep neural network (DNN) for environment-aware speech
dereverberation, which can adaptively... | 2103.16849 | title_snapshot |
gu21_interspeech | Residual Echo and Noise Cancellation with Feature Attention Module and Multi-Domain Loss Function | [
"Jianjun Gu",
"Longbiao Cheng",
"Xingwei Sun",
"Junfeng Li",
"Yonghong Yan"
] | https://www.isca-archive.org/interspeech_2021/gu21_interspeech.html | https://www.isca-archive.org/interspeech_2021/gu21_interspeech.pdf | 10.21437/Interspeech.2021-538 | 1114-1118 | @inproceedings{gu21_interspeech,
title = {{Residual Echo and Noise Cancellation with Feature Attention Module and Multi-Domain Loss Function}},
author = {Jianjun Gu and Longbiao Cheng and Xingwei Sun and Junfeng Li and Yonghong Yan},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {11... | For real-time acoustic echo cancellation in noisy environments, the
classical linear adaptive filters (LAFs) can only remove the linear
components of acoustic echo. To further attenuate the non-linear echo
components and background noise, this paper proposes a deep learning-based
residual echo and noise cancellation (R... | null | null |
li21c_interspeech | MIMO Self-Attentive RNN Beamformer for Multi-Speaker Speech Separation | [
"Xiyun Li",
"Yong Xu",
"Meng Yu",
"Shi-Xiong Zhang",
"Jiaming Xu",
"Bo Xu",
"Dong Yu"
] | https://www.isca-archive.org/interspeech_2021/li21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21c_interspeech.pdf | 10.21437/Interspeech.2021-570 | 1119-1123 | @inproceedings{li21c_interspeech,
title = {{MIMO Self-Attentive RNN Beamformer for Multi-Speaker Speech Separation}},
author = {Xiyun Li and Yong Xu and Meng Yu and Shi-Xiong Zhang and Jiaming Xu and Bo Xu and Dong Yu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1119--1123},
d... | Recently, our proposed recurrent neural network (RNN) based all deep
learning minimum variance distortionless response (ADL-MVDR) beamformer
method yielded superior performance over the conventional MVDR by replacing
the matrix inversion and eigenvalue decomposition with two RNNs. In
this work, we present a self-attent... | 2104.08450 | title_snapshot |
giri21_interspeech | Personalized PercepNet: Real-Time, Low-Complexity Target Voice Separation and Enhancement | [
"Ritwik Giri",
"Shrikant Venkataramani",
"Jean-Marc Valin",
"Umut Isik",
"Arvindh Krishnaswamy"
] | https://www.isca-archive.org/interspeech_2021/giri21_interspeech.html | https://www.isca-archive.org/interspeech_2021/giri21_interspeech.pdf | 10.21437/Interspeech.2021-694 | 1124-1128 | @inproceedings{giri21_interspeech,
title = {{Personalized PercepNet: Real-Time, Low-Complexity Target Voice Separation and Enhancement}},
author = {Ritwik Giri and Shrikant Venkataramani and Jean-Marc Valin and Umut Isik and Arvindh Krishnaswamy},
year = {2021},
booktitle = {{Interspeech 2021}},
p... | The presence of multiple talkers in the surrounding environment poses
a difficult challenge for real-time speech communication systems considering
the constraints on network size and complexity. In this paper, we present
Personalized PercepNet, a real-time speech enhancement model that separates
a target speaker from a... | 2106.04129 | title_snapshot |
yemini21_interspeech | Scene-Agnostic Multi-Microphone Speech Dereverberation | [
"Yochai Yemini",
"Ethan Fetaya",
"Haggai Maron",
"Sharon Gannot"
] | https://www.isca-archive.org/interspeech_2021/yemini21_interspeech.html | https://www.isca-archive.org/interspeech_2021/yemini21_interspeech.pdf | 10.21437/Interspeech.2021-889 | 1129-1133 | @inproceedings{yemini21_interspeech,
title = {{Scene-Agnostic Multi-Microphone Speech Dereverberation}},
author = {Yochai Yemini and Ethan Fetaya and Haggai Maron and Sharon Gannot},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1129--1133},
doi = {10.21437/Interspeech.2021... | Neural networks (NNs) have been widely applied in speech processing
tasks, and, in particular, those employing microphone arrays. Nevertheless,
most existing NN architectures can only deal with fixed and position-specific
microphone arrays. In this paper, we present an NN architecture that
can cope with microphone arra... | 2010.11875 | title_snapshot |
tanaka21_interspeech | Manifold-Aware Deep Clustering: Maximizing Angles Between Embedding Vectors Based on Regular Simplex | [
"Keitaro Tanaka",
"Ryosuke Sawata",
"Shusuke Takahashi"
] | https://www.isca-archive.org/interspeech_2021/tanaka21_interspeech.html | https://www.isca-archive.org/interspeech_2021/tanaka21_interspeech.pdf | 10.21437/Interspeech.2021-1029 | 1134-1138 | @inproceedings{tanaka21_interspeech,
title = {{Manifold-Aware Deep Clustering: Maximizing Angles Between Embedding Vectors Based on Regular Simplex}},
author = {Keitaro Tanaka and Ryosuke Sawata and Shusuke Takahashi},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1134--1138},
do... | This paper presents a new deep clustering (DC) method called manifold-aware
DC (M-DC) that can enhance hyperspace utilization more effectively
than the original DC. The original DC has a limitation in that a pair
of two speakers has to be embedded having an orthogonal relationship
due to its use of the one-hot vector-b... | 2106.02331 | title_snapshot |
zhang21h_interspeech | A Deep Learning Approach to Multi-Channel and Multi-Microphone Acoustic Echo Cancellation | [
"Hao Zhang",
"DeLiang Wang"
] | https://www.isca-archive.org/interspeech_2021/zhang21h_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21h_interspeech.pdf | 10.21437/Interspeech.2021-1508 | 1139-1143 | @inproceedings{zhang21h_interspeech,
title = {{A Deep Learning Approach to Multi-Channel and Multi-Microphone Acoustic Echo Cancellation}},
author = {Hao Zhang and DeLiang Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1139--1143},
doi = {10.21437/Interspeech.2021-150... | Building on deep learning based acoustic echo cancellation (AEC) in
the single-loudspeaker (single-channel) and single-microphone setup,
this paper investigates multi-channel (multi-loudspeaker) AEC (MCAEC)
and multi-microphone AEC (MMAEC). A convolutional recurrent network
(CRN) is trained to predict the near-end spee... | 2103.02552 | title_judge |
na21_interspeech | Joint Online Multichannel Acoustic Echo Cancellation, Speech Dereverberation and Source Separation | [
"Yueyue Na",
"Ziteng Wang",
"Zhang Liu",
"Biao Tian",
"Qiang Fu"
] | https://www.isca-archive.org/interspeech_2021/na21_interspeech.html | https://www.isca-archive.org/interspeech_2021/na21_interspeech.pdf | 10.21437/Interspeech.2021-1950 | 1144-1148 | @inproceedings{na21_interspeech,
title = {{Joint Online Multichannel Acoustic Echo Cancellation, Speech Dereverberation and Source Separation}},
author = {Yueyue Na and Ziteng Wang and Zhang Liu and Biao Tian and Qiang Fu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1144--1148},... | This paper presents a joint source separation algorithm that simultaneously
reduces acoustic echo, reverberation and interfering sources. Target
speeches are separated from the mixture by maximizing independence
with respect to the other sources. It is shown that the separation
process can be decomposed into cascading ... | 2104.04325 | title_snapshot |
sato21_interspeech | Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition | [
"Hiroshi Sato",
"Tsubasa Ochiai",
"Marc Delcroix",
"Keisuke Kinoshita",
"Takafumi Moriya",
"Naoyuki Kamo"
] | https://www.isca-archive.org/interspeech_2021/sato21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sato21_interspeech.pdf | 10.21437/Interspeech.2021-2253 | 1149-1153 | @inproceedings{sato21_interspeech,
title = {{Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition}},
author = {Hiroshi Sato and Tsubasa Ochiai and Marc Delcroix and Keisuke Kinoshita and Takafumi Moriya and Naoyuki Kamo},
year = {2021},
bookt... | Although recent advances in deep learning technology improved automatic
speech recognition (ASR), it remains difficult to recognize speech
when it overlaps other people’s voices. Speech separation or
extraction is often used as a front-end to ASR to handle such overlapping
speech. However, deep neural network-based spe... | 2106.00949 | title_snapshot |
udupa21_interspeech | Estimating Articulatory Movements in Speech Production with Transformer Networks | [
"Sathvik Udupa",
"Anwesha Roy",
"Abhayjeet Singh",
"Aravind Illa",
"Prasanta Kumar Ghosh"
] | https://www.isca-archive.org/interspeech_2021/udupa21_interspeech.html | https://www.isca-archive.org/interspeech_2021/udupa21_interspeech.pdf | 10.21437/Interspeech.2021-1375 | 1154-1158 | @inproceedings{udupa21_interspeech,
title = {{Estimating Articulatory Movements in Speech Production with Transformer Networks}},
author = {Sathvik Udupa and Anwesha Roy and Abhayjeet Singh and Aravind Illa and Prasanta Kumar Ghosh},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {11... | We estimate articulatory movements in speech production from different
modalities - acoustics and phonemes. Acoustic-to-articulatory inversion
(AAI) is a sequence-to-sequence task. On the other hand, phoneme to
articulatory (PTA) motion estimation faces a key challenge in reliably
aligning the text and the articulatory... | 2104.05017 | title_snapshot |
yang21b_interspeech | Unsupervised Multi-Target Domain Adaptation for Acoustic Scene Classification | [
"Dongchao Yang",
"Helin Wang",
"Yuexian Zou"
] | https://www.isca-archive.org/interspeech_2021/yang21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/yang21b_interspeech.pdf | 10.21437/Interspeech.2021-300 | 1159-1163 | @inproceedings{yang21b_interspeech,
title = {{Unsupervised Multi-Target Domain Adaptation for Acoustic Scene Classification}},
author = {Dongchao Yang and Helin Wang and Yuexian Zou},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1159--1163},
doi = {10.21437/Interspeech.202... | It is well known that the mismatch between training (source) and test
(target) data distribution will significantly decrease the performance
of acoustic scene classification (ASC) systems. To address this issue,
domain adaptation (DA) is one solution and many unsupervised DA methods
have been proposed. These methods fo... | 2105.10340 | title_snapshot |
jaramillo21_interspeech | Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation | [
"Alfredo Esquivel Jaramillo",
"Jesper Kjær Nielsen",
"Mads Græsbøll Christensen"
] | https://www.isca-archive.org/interspeech_2021/jaramillo21_interspeech.html | https://www.isca-archive.org/interspeech_2021/jaramillo21_interspeech.pdf | 10.21437/Interspeech.2021-47 | 1164-1168 | @inproceedings{jaramillo21_interspeech,
title = {{Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation}},
author = {Alfredo Esquivel Jaramillo and Jesper Kjær Nielsen and Mads Græsbøll Christensen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1164--1168},
... | In a hybrid speech model, both voiced and unvoiced components can coexist
in a segment. Often, the voiced speech is regarded as the deterministic
component, and the unvoiced speech and additive noise are the stochastic
components. Typically, the speech signal is considered stationary within
fixed segments of 20–40 ms, ... | 2105.01302 | title_snapshot |
luo21b_interspeech | Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation | [
"Jian Luo",
"Jianzong Wang",
"Ning Cheng",
"Jing Xiao"
] | https://www.isca-archive.org/interspeech_2021/luo21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/luo21b_interspeech.pdf | 10.21437/Interspeech.2021-1066 | 1169-1173 | @inproceedings{luo21b_interspeech,
title = {{Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation}},
author = {Jian Luo and Jianzong Wang and Ning Cheng and Jing Xiao},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1169--1173},
doi ... | Predicting the altered acoustic frames is an effective way of self-supervised
learning for speech representation. However, it is challenging to prevent
the pretrained model from overfitting. In this paper, we proposed to
introduce two dropout regularization methods into the pretraining of
transformer encoder: (1) atten... | 2107.04227 | title_snapshot |
yarra21_interspeech | Noise Robust Pitch Stylization Using Minimum Mean Absolute Error Criterion | [
"Chiranjeevi Yarra",
"Prasanta Kumar Ghosh"
] | https://www.isca-archive.org/interspeech_2021/yarra21_interspeech.html | https://www.isca-archive.org/interspeech_2021/yarra21_interspeech.pdf | 10.21437/Interspeech.2021-1307 | 1174-1178 | @inproceedings{yarra21_interspeech,
title = {{Noise Robust Pitch Stylization Using Minimum Mean Absolute Error Criterion}},
author = {Chiranjeevi Yarra and Prasanta Kumar Ghosh},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1174--1178},
doi = {10.21437/Interspeech.2021-130... | We propose a pitch stylization technique in the presence of pitch halving
and doubling errors. The technique uses an optimization criterion based
on a minimum mean absolute error to make the stylization robust to
such pitch estimation errors, particularly under noisy conditions.
We obtain segments for the stylization a... | null | null |
huang21b_interspeech | An Attribute-Aligned Strategy for Learning Speech Representation | [
"Yu-Lin Huang",
"Bo-Hao Su",
"Y.-W. Peter Hong",
"Chi-Chun Lee"
] | https://www.isca-archive.org/interspeech_2021/huang21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/huang21b_interspeech.pdf | 10.21437/Interspeech.2021-1341 | 1179-1183 | @inproceedings{huang21b_interspeech,
title = {{An Attribute-Aligned Strategy for Learning Speech Representation}},
author = {Yu-Lin Huang and Bo-Hao Su and Y.-W. Peter Hong and Chi-Chun Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1179--1183},
doi = {10.21437/Intersp... | Advancement in speech technology has brought convenience to our life.
However, the concern is on the rise as speech signal contains multiple
personal attributes, which would lead to either sensitive information
leakage or bias toward decision. In this work, we propose an attribute-aligned
learning strategy to derive sp... | 2106.02810 | title_snapshot |
shahrebabaki21_interspeech | Raw Speech-to-Articulatory Inversion by Temporal Filtering and Decimation | [
"Abdolreza Sabzi Shahrebabaki",
"Sabato Marco Siniscalchi",
"Torbjørn Svendsen"
] | https://www.isca-archive.org/interspeech_2021/shahrebabaki21_interspeech.html | https://www.isca-archive.org/interspeech_2021/shahrebabaki21_interspeech.pdf | 10.21437/Interspeech.2021-1429 | 1184-1188 | @inproceedings{shahrebabaki21_interspeech,
title = {{Raw Speech-to-Articulatory Inversion by Temporal Filtering and Decimation}},
author = {Abdolreza Sabzi Shahrebabaki and Sabato Marco Siniscalchi and Torbjørn Svendsen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1184--1188},
... | We propose a novel sequence-to-sequence acoustic-to-articulatory inversion
(AAI) neural architecture in the temporal waveform domain. In contrast
to traditional AAI approaches that leverage hand-crafted short-time
spectral features obtained from the windowed signal, such as LSFs,
or MFCCs, our solution directly process... | null | null |
lilley21_interspeech | Unsupervised Training of a DNN-Based Formant Tracker | [
"Jason Lilley",
"H. Timothy Bunnell"
] | https://www.isca-archive.org/interspeech_2021/lilley21_interspeech.html | https://www.isca-archive.org/interspeech_2021/lilley21_interspeech.pdf | 10.21437/Interspeech.2021-1690 | 1189-1193 | @inproceedings{lilley21_interspeech,
title = {{Unsupervised Training of a DNN-Based Formant Tracker}},
author = {Jason Lilley and H. Timothy Bunnell},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1189--1193},
doi = {10.21437/Interspeech.2021-1690},
issn = {2958-1796... | Phonetic analysis often requires reliable estimation of formants, but
estimates provided by popular programs can be unreliable. Recently,
Dissen et al. [1] described DNN-based formant trackers that produced
more accurate frequency estimates than several others, but require
manually-corrected formant data for training. ... | null | null |
yang21c_interspeech | SUPERB: Speech Processing Universal PERformance Benchmark | [
"Shu-wen Yang",
"Po-Han Chi",
"Yung-Sung Chuang",
"Cheng-I Jeff Lai",
"Kushal Lakhotia",
"Yist Y. Lin",
"Andy T. Liu",
"Jiatong Shi",
"Xuankai Chang",
"Guan-Ting Lin",
"Tzu-Hsien Huang",
"Wei-Cheng Tseng",
"Ko-tik Lee",
"Da-Rong Liu",
"Zili Huang",
"Shuyan Dong",
"Shang-Wen Li",
"S... | https://www.isca-archive.org/interspeech_2021/yang21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/yang21c_interspeech.pdf | 10.21437/Interspeech.2021-1775 | 1194-1198 | @inproceedings{yang21c_interspeech,
title = {{SUPERB: Speech Processing Universal PERformance Benchmark}},
author = {Shu-wen Yang and Po-Han Chi and Yung-Sung Chuang and Cheng-I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan-Ting Lin and Tzu-Hsien Huan... | Self-supervised learning (SSL) has proven vital for advancing research
in natural language processing (NLP) and computer vision (CV). The
paradigm pretrains a shared model on large volumes of unlabeled
data and achieves state-of-the-art (SOTA) for various tasks with
minimal adaptation . However, the speech processing c... | 2105.01051 | title_snapshot |
zhang21i_interspeech | Synchronising Speech Segments with Musical Beats in Mandarin and English Singing | [
"Cong Zhang",
"Jian Zhu"
] | https://www.isca-archive.org/interspeech_2021/zhang21i_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21i_interspeech.pdf | 10.21437/Interspeech.2021-1841 | 1199-1203 | @inproceedings{zhang21i_interspeech,
title = {{Synchronising Speech Segments with Musical Beats in Mandarin and English Singing}},
author = {Cong Zhang and Jian Zhu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1199--1203},
doi = {10.21437/Interspeech.2021-1841},
issn ... | Generating synthesised singing voice with models trained on speech
data has many advantages due to the models’ flexibility and controllability.
However, since the information about the temporal relationship between
segments and beats are lacking in speech training data, the synthesised
singing may sound off-beat at tim... | 2106.10045 | title_snapshot |
peplinski21_interspeech | FRILL: A Non-Semantic Speech Embedding for Mobile Devices | [
"Jacob Peplinski",
"Joel Shor",
"Sachin Joglekar",
"Jake Garrison",
"Shwetak Patel"
] | https://www.isca-archive.org/interspeech_2021/peplinski21_interspeech.html | https://www.isca-archive.org/interspeech_2021/peplinski21_interspeech.pdf | 10.21437/Interspeech.2021-2070 | 1204-1208 | @inproceedings{peplinski21_interspeech,
title = {{FRILL: A Non-Semantic Speech Embedding for Mobile Devices}},
author = {Jacob Peplinski and Joel Shor and Sachin Joglekar and Jake Garrison and Shwetak Patel},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1204--1208},
doi = ... | Learned speech representations can drastically improve performance
on tasks with limited labeled data. However, due to their size and
complexity, learned representations have limited utility in mobile
settings where run-time performance can be a significant bottleneck.
In this work, we propose a class of lightweight no... | 2011.04609 | title_snapshot |
mori21_interspeech | Pitch Contour Separation from Overlapping Speech | [
"Hiroki Mori"
] | https://www.isca-archive.org/interspeech_2021/mori21_interspeech.html | https://www.isca-archive.org/interspeech_2021/mori21_interspeech.pdf | 10.21437/Interspeech.2021-2164 | 1209-1213 | @inproceedings{mori21_interspeech,
title = {{Pitch Contour Separation from Overlapping Speech}},
author = {Hiroki Mori},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1209--1213},
doi = {10.21437/Interspeech.2021-2164},
issn = {2958-1796},
} | In everyday conversation, speakers’ utterances often overlap.
For conversation corpora that are recorded in diverse environments,
results of pitch extraction in the overlapping parts may be incorrect.
The goal of this study is to establish the technique of separating
each speaker’s pitch contour from an overlapping spe... | null | null |
kumar21_interspeech | Do Sound Event Representations Generalize to Other Audio Tasks? A Case Study in Audio Transfer Learning | [
"Anurag Kumar",
"Yun Wang",
"Vamsi Krishna Ithapu",
"Christian Fuegen"
] | https://www.isca-archive.org/interspeech_2021/kumar21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kumar21_interspeech.pdf | 10.21437/Interspeech.2021-347 | 1214-1218 | @inproceedings{kumar21_interspeech,
title = {{Do Sound Event Representations Generalize to Other Audio Tasks? A Case Study in Audio Transfer Learning}},
author = {Anurag Kumar and Yun Wang and Vamsi Krishna Ithapu and Christian Fuegen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = ... | Transfer learning is critical for efficient information transfer across
multiple related learning problems. A simple, yet effective transfer
learning approach utilizes deep neural networks trained on a large-scale
task for feature extraction. Such representations are then used to
learn related downstream tasks. In this... | 2106.11335 | title_snapshot |
peng21b_interspeech | Data Augmentation for Spoken Language Understanding via Pretrained Language Models | [
"Baolin Peng",
"Chenguang Zhu",
"Michael Zeng",
"Jianfeng Gao"
] | https://www.isca-archive.org/interspeech_2021/peng21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/peng21b_interspeech.pdf | 10.21437/Interspeech.2021-117 | 1219-1223 | @inproceedings{peng21b_interspeech,
title = {{Data Augmentation for Spoken Language Understanding via Pretrained Language Models}},
author = {Baolin Peng and Chenguang Zhu and Michael Zeng and Jianfeng Gao},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1219--1223},
doi = {... | The training of spoken language understanding (SLU) models often faces
the problem of data scarcity. In this paper, we put forward a data
augmentation method using pretrained language models to boost the variability
and accuracy of generated utterances. Furthermore, we investigate and
propose solutions to two previousl... | 2004.13952 | title_snapshot |
radfar21_interspeech | FANS: Fusing ASR and NLU for On-Device SLU | [
"Martin Radfar",
"Athanasios Mouchtaris",
"Siegfried Kunzmann",
"Ariya Rastrow"
] | https://www.isca-archive.org/interspeech_2021/radfar21_interspeech.html | https://www.isca-archive.org/interspeech_2021/radfar21_interspeech.pdf | 10.21437/Interspeech.2021-793 | 1224-1228 | @inproceedings{radfar21_interspeech,
title = {{FANS: Fusing ASR and NLU for On-Device SLU}},
author = {Martin Radfar and Athanasios Mouchtaris and Siegfried Kunzmann and Ariya Rastrow},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1224--1228},
doi = {10.21437/Interspeech.2... | Spoken language understanding (SLU) systems translate voice input commands
to semantics which are encoded as an intent and pairs of slot tags
and values. Most current SLU systems deploy a cascade of two neural
models where the first one maps the input audio to a transcript (ASR)
and the second predicts the intent and s... | 2111.00400 | title_snapshot |
cao21c_interspeech | Sequential End-to-End Intent and Slot Label Classification and Localization | [
"Yiran Cao",
"Nihal Potdar",
"Anderson R. Avila"
] | https://www.isca-archive.org/interspeech_2021/cao21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/cao21c_interspeech.pdf | 10.21437/Interspeech.2021-1569 | 1229-1233 | @inproceedings{cao21c_interspeech,
title = {{Sequential End-to-End Intent and Slot Label Classification and Localization}},
author = {Yiran Cao and Nihal Potdar and Anderson R. Avila},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1229--1233},
doi = {10.21437/Interspeech.20... | Human-computer interaction (HCI) is significantly impacted by delayed
responses from a spoken dialogue system. Hence, end-to-end (e2e) spoken
language understanding (SLU) solutions have recently been proposed
to decrease latency. Such approaches allow for the extraction of semantic
information directly from the speech ... | 2106.04660 | title_snapshot |
muralidharan21_interspeech | DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants | [
"Deepak Muralidharan",
"Joel Ruben Antony Moniz",
"Weicheng Zhang",
"Stephen Pulman",
"Lin Li",
"Megan Barnes",
"Jingjing Pan",
"Jason Williams",
"Alex Acero"
] | https://www.isca-archive.org/interspeech_2021/muralidharan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/muralidharan21_interspeech.pdf | 10.21437/Interspeech.2021-1877 | 1234-1238 | @inproceedings{muralidharan21_interspeech,
title = {{DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants}},
author = {Deepak Muralidharan and Joel Ruben Antony Moniz and Weicheng Zhang and Stephen Pulman and Lin Li and Megan Barnes and Jingjing Pan and Jason William... | Named entity recognition (NER) is usually developed and tested on text
from well-written sources. However, in intelligent voice assistants,
where NER is an important component, input to NER may be noisy because
of user or speech recognition error. In applications, entity labels
may change frequently, and non-textual pr... | 2108.06633 | title_snapshot |
wu21d_interspeech | A Context-Aware Hierarchical BERT Fusion Network for Multi-Turn Dialog Act Detection | [
"Ting-Wei Wu",
"Ruolin Su",
"Biing-Hwang Juang"
] | https://www.isca-archive.org/interspeech_2021/wu21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/wu21d_interspeech.pdf | 10.21437/Interspeech.2021-95 | 1239-1243 | @inproceedings{wu21d_interspeech,
title = {{A Context-Aware Hierarchical BERT Fusion Network for Multi-Turn Dialog Act Detection}},
author = {Ting-Wei Wu and Ruolin Su and Biing-Hwang Juang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1239--1243},
doi = {10.21437/Intersp... | The success of interactive dialog systems is usually associated with
the quality of the spoken language understanding (SLU) task, which
mainly identifies the corresponding dialog acts and slot values in
each turn. By treating utterances in isolation, most SLU systems often
overlook the semantic context in which a dialo... | 2109.01267 | title_snapshot |
chen21g_interspeech | Pre-Training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning | [
"Qian Chen",
"Wen Wang",
"Qinglin Zhang"
] | https://www.isca-archive.org/interspeech_2021/chen21g_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21g_interspeech.pdf | 10.21437/Interspeech.2021-234 | 1244-1248 | @inproceedings{chen21g_interspeech,
title = {{Pre-Training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning}},
author = {Qian Chen and Wen Wang and Qinglin Zhang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1244--1248},
doi = {10.... | In the traditional cascading architecture for spoken language understanding
(SLU), it has been observed that automatic speech recognition errors
could be detrimental to the performance of natural language understanding.
End-to-end (E2E) SLU models have been proposed to directly map speech
input to desired semantic fram... | 2104.10357 | title_snapshot |
do21b_interspeech | Predicting Temporal Performance Drop of Deployed Production Spoken Language Understanding Models | [
"Quynh Do",
"Judith Gaspers",
"Daniil Sorokin",
"Patrick Lehnen"
] | https://www.isca-archive.org/interspeech_2021/do21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/do21b_interspeech.pdf | 10.21437/Interspeech.2021-580 | 1249-1253 | @inproceedings{do21b_interspeech,
title = {{Predicting Temporal Performance Drop of Deployed Production Spoken Language Understanding Models}},
author = {Quynh Do and Judith Gaspers and Daniil Sorokin and Patrick Lehnen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1249--1253},
... | In deployed real-world spoken language understanding (SLU) applications,
data continuously flows into the system. This leads to distributional
differences between training and application data that can deteriorate
model performance. While regularly retraining the deployed model with
new data helps mitigating this probl... | null | null |
ganhotra21_interspeech | Integrating Dialog History into End-to-End Spoken Language Understanding Systems | [
"Jatin Ganhotra",
"Samuel Thomas",
"Hong-Kwang J. Kuo",
"Sachindra Joshi",
"George Saon",
"Zoltán Tüske",
"Brian Kingsbury"
] | https://www.isca-archive.org/interspeech_2021/ganhotra21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ganhotra21_interspeech.pdf | 10.21437/Interspeech.2021-1460 | 1254-1258 | @inproceedings{ganhotra21_interspeech,
title = {{Integrating Dialog History into End-to-End Spoken Language Understanding Systems}},
author = {Jatin Ganhotra and Samuel Thomas and Hong-Kwang J. Kuo and Sachindra Joshi and George Saon and Zoltán Tüske and Brian Kingsbury},
year = {2021},
booktitle = ... | End-to-end spoken language understanding (SLU) systems that process
human-human or human-computer interactions are often context independent
and process each turn of a conversation independently. Spoken conversations
on the other hand, are very much context dependent, and dialog history
contains useful information that... | 2108.08405 | title_snapshot |
han21_interspeech | Coreference Augmentation for Multi-Domain Task-Oriented Dialogue State Tracking | [
"Ting Han",
"Chongxuan Huang",
"Wei Peng"
] | https://www.isca-archive.org/interspeech_2021/han21_interspeech.html | https://www.isca-archive.org/interspeech_2021/han21_interspeech.pdf | 10.21437/Interspeech.2021-1463 | 1259-1263 | @inproceedings{han21_interspeech,
title = {{Coreference Augmentation for Multi-Domain Task-Oriented Dialogue State Tracking}},
author = {Ting Han and Chongxuan Huang and Wei Peng},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1259--1263},
doi = {10.21437/Interspeech.2021-1... | Dialogue State Tracking (DST), which is the process of inferring user
goals by estimating belief states given the dialogue history, plays
a critical role in task-oriented dialogue systems. A coreference phenomenon
observed in multi-turn conversations is not addressed by existing DST
models, leading to suboptimal perfor... | 2106.08723 | title_snapshot |
arora21_interspeech | Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding | [
"Siddhant Arora",
"Alissa Ostapenko",
"Vijay Viswanathan",
"Siddharth Dalmia",
"Florian Metze",
"Shinji Watanabe",
"Alan W. Black"
] | https://www.isca-archive.org/interspeech_2021/arora21_interspeech.html | https://www.isca-archive.org/interspeech_2021/arora21_interspeech.pdf | 10.21437/Interspeech.2021-1537 | 1264-1268 | @inproceedings{arora21_interspeech,
title = {{Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding}},
author = {Siddhant Arora and Alissa Ostapenko and Vijay Viswanathan and Siddharth Dalmia and Florian Metze and Shinji Watanabe and Alan W. Black},
year ... | Decomposable tasks are complex and comprise of a hierarchy of sub-tasks.
Spoken intent prediction, for example, combines automatic speech recognition
and natural language understanding. Existing benchmarks, however, typically
hold out examples for only the surface-level sub-task. As a result,
models with similar perfor... | 2106.15065 | title_snapshot |
sun21b_interspeech | Semantic Data Augmentation for End-to-End Mandarin Speech Recognition | [
"Jianwei Sun",
"Zhiyuan Tang",
"Hengxin Yin",
"Wei Wang",
"Xi Zhao",
"Shuaijiang Zhao",
"Xiaoning Lei",
"Wei Zou",
"Xiangang Li"
] | https://www.isca-archive.org/interspeech_2021/sun21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/sun21b_interspeech.pdf | 10.21437/Interspeech.2021-1162 | 1269-1273 | @inproceedings{sun21b_interspeech,
title = {{Semantic Data Augmentation for End-to-End Mandarin Speech Recognition}},
author = {Jianwei Sun and Zhiyuan Tang and Hengxin Yin and Wei Wang and Xi Zhao and Shuaijiang Zhao and Xiaoning Lei and Wei Zou and Xiangang Li},
year = {2021},
booktitle = {{Inters... | End-to-end models have gradually become the preferred option for automatic
speech recognition (ASR) applications. During the training of end-to-end
ASR, data augmentation is a quite effective technique for regularizing
the neural networks. This paper proposes a novel data augmentation
technique based on semantic transp... | 2104.12521 | title_snapshot |
gong21c_interspeech | Layer-Wise Fast Adaptation for End-to-End Multi-Accent Speech Recognition | [
"Xun Gong",
"Yizhou Lu",
"Zhikai Zhou",
"Yanmin Qian"
] | https://www.isca-archive.org/interspeech_2021/gong21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/gong21c_interspeech.pdf | 10.21437/Interspeech.2021-1075 | 1274-1278 | @inproceedings{gong21c_interspeech,
title = {{Layer-Wise Fast Adaptation for End-to-End Multi-Accent Speech Recognition}},
author = {Xun Gong and Yizhou Lu and Zhikai Zhou and Yanmin Qian},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1274--1278},
doi = {10.21437/Interspee... | Accent variability has posed a huge challenge to automatic speech recognition
(ASR) modeling. Although one-hot accent vector based adaptation systems
are commonly used, they require prior knowledge about the target accent
and cannot handle unseen accents. Furthermore, simply concatenating
accent embeddings does not mak... | 2204.09883 | title_snapshot |
wang21m_interspeech | Low Resource German ASR with Untranscribed Data Spoken by Non-Native Children — INTERSPEECH 2021 Shared Task SPAPL System | [
"Jinhan Wang",
"Yunzheng Zhu",
"Ruchao Fan",
"Wei Chu",
"Abeer Alwan"
] | https://www.isca-archive.org/interspeech_2021/wang21m_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21m_interspeech.pdf | 10.21437/Interspeech.2021-1974 | 1279-1283 | @inproceedings{wang21m_interspeech,
title = {{Low Resource German ASR with Untranscribed Data Spoken by Non-Native Children — INTERSPEECH 2021 Shared Task SPAPL System}},
author = {Jinhan Wang and Yunzheng Zhu and Ruchao Fan and Wei Chu and Abeer Alwan},
year = {2021},
booktitle = {{Interspeech 2021... | This paper describes the SPAPL system for the INTERSPEECH 2021 Challenge:
Shared Task on Automatic Speech Recognition for Non-Native Children’s
Speech in German. ~5 hours of transcribed data and ~60 hours
of untranscribed data are provided to develop a German ASR system for
children. For the training of the transcribed... | 2106.09963 | title_snapshot |
sim21_interspeech | Robust Continuous On-Device Personalization for Automatic Speech Recognition | [
"Khe Chai Sim",
"Angad Chandorkar",
"Fan Gao",
"Mason Chua",
"Tsendsuren Munkhdalai",
"Françoise Beaufays"
] | https://www.isca-archive.org/interspeech_2021/sim21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sim21_interspeech.pdf | 10.21437/Interspeech.2021-318 | 1284-1288 | @inproceedings{sim21_interspeech,
title = {{Robust Continuous On-Device Personalization for Automatic Speech Recognition}},
author = {Khe Chai Sim and Angad Chandorkar and Fan Gao and Mason Chua and Tsendsuren Munkhdalai and Françoise Beaufays},
year = {2021},
booktitle = {{Interspeech 2021}},
pag... | On-device personalization of an all-neural automatic speech recognition
(ASR) model can be achieved efficiently by fine-tuning the last few
layers of the model. This approach has been shown to be effective for
adapting the model to recognize rare named entities using only a small
amount of data. To reliably perform con... | null | null |
kumar21b_interspeech | Speaker Normalization Using Joint Variational Autoencoder | [
"Shashi Kumar",
"Shakti P. Rath",
"Abhishek Pandey"
] | https://www.isca-archive.org/interspeech_2021/kumar21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/kumar21b_interspeech.pdf | 10.21437/Interspeech.2021-467 | 1289-1293 | @inproceedings{kumar21b_interspeech,
title = {{Speaker Normalization Using Joint Variational Autoencoder}},
author = {Shashi Kumar and Shakti P. Rath and Abhishek Pandey},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1289--1293},
doi = {10.21437/Interspeech.2021-467},
is... | Speaker adaptation is known to provide significant improvement in speech
recognition accuracy. However, in practical scenario, only a few seconds
of audio is available due to which it may be infeasible to apply speaker
adaptation methods such as i-vector and fMLLR robustly. Also, decoding
with fMLLR transformation happ... | null | null |
xu21c_interspeech | The TAL System for the INTERSPEECH2021 Shared Task on Automatic Speech Recognition for Non-Native Childrens Speech | [
"Gaopeng Xu",
"Song Yang",
"Lu Ma",
"Chengfei Li",
"Zhongqin Wu"
] | https://www.isca-archive.org/interspeech_2021/xu21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/xu21c_interspeech.pdf | 10.21437/Interspeech.2021-1104 | 1294-1298 | @inproceedings{xu21c_interspeech,
title = {{The TAL System for the INTERSPEECH2021 Shared Task on Automatic Speech Recognition for Non-Native Childrens Speech}},
author = {Gaopeng Xu and Song Yang and Lu Ma and Chengfei Li and Zhongqin Wu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | This paper describes TAL’s system for the INTERSPEECH 2021 shared
task on Automatic Speech Recognition (ASR) for non-native children’s
speech. In this work, we attempt to apply the self-supervised approach
to non-native German children’s ASR. First, we conduct some baseline
experiments to indicate that self-supervised ... | null | null |
lam21b_interspeech | On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR | [
"Tsz Kin Lam",
"Mayumi Ohta",
"Shigehiko Schamoni",
"Stefan Riezler"
] | https://www.isca-archive.org/interspeech_2021/lam21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/lam21b_interspeech.pdf | 10.21437/Interspeech.2021-1679 | 1299-1303 | @inproceedings{lam21b_interspeech,
title = {{On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR}},
author = {Tsz Kin Lam and Mayumi Ohta and Shigehiko Schamoni and Stefan Riezler},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1299--1303},
doi = {10.21437/Int... | We propose an on-the-fly data augmentation method for automatic speech
recognition (ASR) that uses alignment information to generate effective
training samples. Our method, called Aligned Data Augmentation (ADA)
for ASR, replaces transcribed tokens and the speech representations
in an aligned manner to generate previou... | 2104.01393 | title_snapshot |
gao21_interspeech | Zero-Shot Cross-Lingual Phonetic Recognition with External Language Embedding | [
"Heting Gao",
"Junrui Ni",
"Yang Zhang",
"Kaizhi Qian",
"Shiyu Chang",
"Mark Hasegawa-Johnson"
] | https://www.isca-archive.org/interspeech_2021/gao21_interspeech.html | https://www.isca-archive.org/interspeech_2021/gao21_interspeech.pdf | 10.21437/Interspeech.2021-1843 | 1304-1308 | @inproceedings{gao21_interspeech,
title = {{Zero-Shot Cross-Lingual Phonetic Recognition with External Language Embedding}},
author = {Heting Gao and Junrui Ni and Yang Zhang and Kaizhi Qian and Shiyu Chang and Mark Hasegawa-Johnson},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1... | Many existing languages are too sparsely resourced for monolingual
deep learning networks to achieve high accuracy. Multilingual phonetic
recognition systems mitigate data sparsity issues by training models
on data from multiple languages and learning a speech-to-phone or speech-to-text
model universal to all languages... | null | null |
huang21c_interspeech | Rapid Speaker Adaptation for Conformer Transducer: Attention and Bias Are All You Need | [
"Yan Huang",
"Guoli Ye",
"Jinyu Li",
"Yifan Gong"
] | https://www.isca-archive.org/interspeech_2021/huang21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/huang21c_interspeech.pdf | 10.21437/Interspeech.2021-1884 | 1309-1313 | @inproceedings{huang21c_interspeech,
title = {{Rapid Speaker Adaptation for Conformer Transducer: Attention and Bias Are All You Need}},
author = {Yan Huang and Guoli Ye and Jinyu Li and Yifan Gong},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1309--1313},
doi = {10.21437... | Conformer transducer achieves new state-of-the-art end-to-end (E2E)
system performance and has become increasingly appealing for production.
In this paper, we study how to effectively perform rapid speaker adaptation
in a conformer transducer and how it compares with the RNN transducer.
We hierarchically decompose the ... | null | null |
das21b_interspeech | Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning | [
"Nilaksh Das",
"Sravan Bodapati",
"Monica Sunkara",
"Sundararajan Srinivasan",
"Duen Horng Chau"
] | https://www.isca-archive.org/interspeech_2021/das21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/das21b_interspeech.pdf | 10.21437/Interspeech.2021-1888 | 1314-1318 | @inproceedings{das21b_interspeech,
title = {{Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning}},
author = {Nilaksh Das and Sravan Bodapati and Monica Sunkara and Sundararajan Srinivasan and Duen Horng Chau},
year = {2021},
booktitle = {{Interspeech 2021}},
... | Training deep neural networks for automatic speech recognition (ASR)
requires large amounts of transcribed speech. This becomes a bottleneck
for training robust models for accented speech which typically
contains high variability in pronunciation and other semantics, since
obtaining large amounts of annotated accented ... | 2103.05834 | title_snapshot |
chu21_interspeech | Extending Pronunciation Dictionary with Automatically Detected Word Mispronunciations to Improve PAII’s System for Interspeech 2021 Non-Native Child English Close Track ASR Challenge | [
"Wei Chu",
"Peng Chang",
"Jing Xiao"
] | https://www.isca-archive.org/interspeech_2021/chu21_interspeech.html | https://www.isca-archive.org/interspeech_2021/chu21_interspeech.pdf | 10.21437/Interspeech.2021-2053 | 1319-1323 | @inproceedings{chu21_interspeech,
title = {{Extending Pronunciation Dictionary with Automatically Detected Word Mispronunciations to Improve PAII’s System for Interspeech 2021 Non-Native Child English Close Track ASR Challenge}},
author = {Wei Chu and Peng Chang and Jing Xiao},
year = {2021},
bookti... | This paper proposed to automatically detect mispronounced words over
the regions that have low Goodness-of-Pronunciation scores through
a constrained phone decoder, then add these word mispronunciations
into the orthodox lexicon without colliding with existing pronunciations,
finally use the expanded lexicon for decodi... | null | null |
li21d_interspeech | CVC: Contrastive Learning for Non-Parallel Voice Conversion | [
"Tingle Li",
"Yichen Liu",
"Chenxu Hu",
"Hang Zhao"
] | https://www.isca-archive.org/interspeech_2021/li21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21d_interspeech.pdf | 10.21437/Interspeech.2021-137 | 1324-1328 | @inproceedings{li21d_interspeech,
title = {{CVC: Contrastive Learning for Non-Parallel Voice Conversion}},
author = {Tingle Li and Yichen Liu and Chenxu Hu and Hang Zhao},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1324--1328},
doi = {10.21437/Interspeech.2021-137},
is... | Cycle consistent generative adversarial network (CycleGAN) and variational
autoencoder (VAE) based models have gained popularity in non-parallel
voice conversion recently. However, they often suffer from difficult
training process and unsatisfactory results. In this paper, we propose
a contrastive learning-based advers... | 2011.00782 | title_snapshot |
huang21d_interspeech | A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion | [
"Wen-Chin Huang",
"Kazuhiro Kobayashi",
"Yu-Huai Peng",
"Ching-Feng Liu",
"Yu Tsao",
"Hsin-Min Wang",
"Tomoki Toda"
] | https://www.isca-archive.org/interspeech_2021/huang21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/huang21d_interspeech.pdf | 10.21437/Interspeech.2021-208 | 1329-1333 | @inproceedings{huang21d_interspeech,
title = {{A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion}},
author = {Wen-Chin Huang and Kazuhiro Kobayashi and Yu-Huai Peng and Ching-Feng Liu and Yu Tsao and Hsin-Min Wang and Tomoki Toda},
year = {2021}... | We propose a new paradigm for maintaining speaker identity in dysarthric
voice conversion (DVC). The poor quality of dysarthric speech can be
greatly improved by statistical VC, but as the normal speech utterances
of a dysarthria patient are nearly impossible to collect, previous
work failed to recover the individualit... | 2106.01415 | title_snapshot |
eskimez21_interspeech | One-Shot Voice Conversion with Speaker-Agnostic StarGAN | [
"Sefik Emre Eskimez",
"Dimitrios Dimitriadis",
"Kenichi Kumatani",
"Robert Gmyr"
] | https://www.isca-archive.org/interspeech_2021/eskimez21_interspeech.html | https://www.isca-archive.org/interspeech_2021/eskimez21_interspeech.pdf | 10.21437/Interspeech.2021-221 | 1334-1338 | @inproceedings{eskimez21_interspeech,
title = {{One-Shot Voice Conversion with Speaker-Agnostic StarGAN}},
author = {Sefik Emre Eskimez and Dimitrios Dimitriadis and Kenichi Kumatani and Robert Gmyr},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1334--1338},
doi = {10.2143... | In this work, we propose a variant of STARGAN for many-to-many voice
conversion (VC) conditioned on the d-vectors for short-duration (2–15
seconds) speech. We make several modifications to the STARGAN training
and employ new network architectures. We employ a transformer encoder
in the discriminator network, and we app... | null | null |
koshizuka21_interspeech | Fine-Tuning Pre-Trained Voice Conversion Model for Adding New Target Speakers with Limited Data | [
"Takeshi Koshizuka",
"Hidefumi Ohmura",
"Kouichi Katsurada"
] | https://www.isca-archive.org/interspeech_2021/koshizuka21_interspeech.html | https://www.isca-archive.org/interspeech_2021/koshizuka21_interspeech.pdf | 10.21437/Interspeech.2021-244 | 1339-1343 | @inproceedings{koshizuka21_interspeech,
title = {{Fine-Tuning Pre-Trained Voice Conversion Model for Adding New Target Speakers with Limited Data}},
author = {Takeshi Koshizuka and Hidefumi Ohmura and Kouichi Katsurada},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1339--1343},
... | Voice conversion (VC) is a technique that converts speaker-dependent
non-linguistic information into that of another speaker, while retaining
the linguistic information of the input speech. A typical VC system
comprises two modules: an encoder module that removes speaker individuality
from the input speech and a decode... | null | null |
wang21n_interspeech | VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-Shot Voice Conversion | [
"Disong Wang",
"Liqun Deng",
"Yu Ting Yeung",
"Xiao Chen",
"Xunying Liu",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2021/wang21n_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21n_interspeech.pdf | 10.21437/Interspeech.2021-283 | 1344-1348 | @inproceedings{wang21n_interspeech,
title = {{VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-Shot Voice Conversion}},
author = {Disong Wang and Liqun Deng and Yu Ting Yeung and Xiao Chen and Xunying Liu and Helen Meng},
year = {2021}... | One-shot voice conversion (VC), which performs conversion across arbitrary
speakers with only a single target-speaker utterance for reference,
can be effectively achieved by speech representation disentanglement.
Existing work generally ignores the correlation between different speech
representations during training, w... | 2106.10132 | title_snapshot |
li21e_interspeech | StarGANv2-VC: A Diverse, Unsupervised, Non-Parallel Framework for Natural-Sounding Voice Conversion | [
"Yinghao Aaron Li",
"Ali Zare",
"Nima Mesgarani"
] | https://www.isca-archive.org/interspeech_2021/li21e_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21e_interspeech.pdf | 10.21437/Interspeech.2021-319 | 1349-1353 | @inproceedings{li21e_interspeech,
title = {{StarGANv2-VC: A Diverse, Unsupervised, Non-Parallel Framework for Natural-Sounding Voice Conversion}},
author = {Yinghao Aaron Li and Ali Zare and Nima Mesgarani},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1349--1353},
doi = {... | We present an unsupervised non-parallel many-to-many voice conversion
(VC) method using a generative adversarial network (GAN) called StarGAN
v2. Using a combination of adversarial source classifier loss and perceptual
loss, our model significantly outperforms previous VC models. Although
our model is trained only with... | 2107.10394 | title_snapshot |
kumar21c_interspeech | Normalization Driven Zero-Shot Multi-Speaker Speech Synthesis | [
"Neeraj Kumar",
"Srishti Goel",
"Ankur Narang",
"Brejesh Lall"
] | https://www.isca-archive.org/interspeech_2021/kumar21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/kumar21c_interspeech.pdf | 10.21437/Interspeech.2021-441 | 1354-1358 | @inproceedings{kumar21c_interspeech,
title = {{Normalization Driven Zero-Shot Multi-Speaker Speech Synthesis}},
author = {Neeraj Kumar and Srishti Goel and Ankur Narang and Brejesh Lall},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1354--1358},
doi = {10.21437/Interspeech... | In this paper, we present a novel zero-shot multi-speaker speech synthesis
approach (ZSM-SS) that leverages the normalization architecture and
speaker encoder with non-autoregressive multi-head attention driven
encoder-decoder architecture. Given an input text and a reference speech
sample of an unseen person, ZSM-SS c... | 2012.07252 | title_judge |
sakamoto21_interspeech | StarGAN-VC+ASR: StarGAN-Based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition | [
"Shoki Sakamoto",
"Akira Taniguchi",
"Tadahiro Taniguchi",
"Hirokazu Kameoka"
] | https://www.isca-archive.org/interspeech_2021/sakamoto21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sakamoto21_interspeech.pdf | 10.21437/Interspeech.2021-492 | 1359-1363 | @inproceedings{sakamoto21_interspeech,
title = {{StarGAN-VC+ASR: StarGAN-Based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition}},
author = {Shoki Sakamoto and Akira Taniguchi and Tadahiro Taniguchi and Hirokazu Kameoka},
year = {2021},
booktitle = {{Interspeech 2021}},
pa... | Preserving the linguistic content of input speech is essential during
voice conversion (VC). The star generative adversarial network-based
VC method (StarGAN-VC) is a recently developed method that allows non-parallel
many-to-many VC. Although this method is powerful, it can fail to preserve
the linguistic content of i... | 2108.04395 | title_snapshot |
xu21d_interspeech | Two-Pathway Style Embedding for Arbitrary Voice Conversion | [
"Xuexin Xu",
"Liang Shi",
"Jinhui Chen",
"Xunquan Chen",
"Jie Lian",
"Pingyuan Lin",
"Zhihong Zhang",
"Edwin R. Hancock"
] | https://www.isca-archive.org/interspeech_2021/xu21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/xu21d_interspeech.pdf | 10.21437/Interspeech.2021-506 | 1364-1368 | @inproceedings{xu21d_interspeech,
title = {{Two-Pathway Style Embedding for Arbitrary Voice Conversion}},
author = {Xuexin Xu and Liang Shi and Jinhui Chen and Xunquan Chen and Jie Lian and Pingyuan Lin and Zhihong Zhang and Edwin R. Hancock},
year = {2021},
booktitle = {{Interspeech 2021}},
pages... | Arbitrary voice conversion, also referred to as zero-shot voice conversion,
has recently attracted increased attention in the literature. Although
disentangling the linguistic and style representations for acoustic
features is an effective way to achieve zero-shot voice conversion,
the problem of how to convert to a na... | null | null |
liu21c_interspeech | Non-Parallel Any-to-Many Voice Conversion by Replacing Speaker Statistics | [
"Yufei Liu",
"Chengzhu Yu",
"Wang Shuai",
"Zhenchuan Yang",
"Yang Chao",
"Weibin Zhang"
] | https://www.isca-archive.org/interspeech_2021/liu21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/liu21c_interspeech.pdf | 10.21437/Interspeech.2021-557 | 1369-1373 | @inproceedings{liu21c_interspeech,
title = {{Non-Parallel Any-to-Many Voice Conversion by Replacing Speaker Statistics}},
author = {Yufei Liu and Chengzhu Yu and Wang Shuai and Zhenchuan Yang and Yang Chao and Weibin Zhang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1369--1373}... | This paper proposes a non-parallel any-to-many voice conversion (VC)
approach with a novel statistics replacement layer. Non-parallel VC
is usually achieved by firstly disentangling linguistic and speaker
representations, and then concatenating the linguistic content with
the learned target speaker’s embedding at the c... | null | null |
zhou21c_interspeech | Cross-Lingual Voice Conversion with a Cycle Consistency Loss on Linguistic Representation | [
"Yi Zhou",
"Xiaohai Tian",
"Zhizheng Wu",
"Haizhou Li"
] | https://www.isca-archive.org/interspeech_2021/zhou21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhou21c_interspeech.pdf | 10.21437/Interspeech.2021-687 | 1374-1378 | @inproceedings{zhou21c_interspeech,
title = {{Cross-Lingual Voice Conversion with a Cycle Consistency Loss on Linguistic Representation}},
author = {Yi Zhou and Xiaohai Tian and Zhizheng Wu and Haizhou Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1374--1378},
doi = {1... | Cross-Lingual Voice Conversion (XVC) aims to modify a source speaker
identity towards a target while preserving the source linguistic content.
This paper introduces a cycle consistency loss on linguistic representation
to ensure the speech content unchanged after conversion. The proposed
XVC model consists of two loss ... | null | null |
du21_interspeech | Improving Robustness of One-Shot Voice Conversion with Deep Discriminative Speaker Encoder | [
"Hongqiang Du",
"Lei Xie"
] | https://www.isca-archive.org/interspeech_2021/du21_interspeech.html | https://www.isca-archive.org/interspeech_2021/du21_interspeech.pdf | 10.21437/Interspeech.2021-2132 | 1379-1383 | @inproceedings{du21_interspeech,
title = {{Improving Robustness of One-Shot Voice Conversion with Deep Discriminative Speaker Encoder}},
author = {Hongqiang Du and Lei Xie},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1379--1383},
doi = {10.21437/Interspeech.2021-2132},
... | One-shot voice conversion has received significant attention since
only one utterance from source speaker and target speaker respectively
is required. Moreover, source speaker and target speaker do not need
to be seen during training. However, available one-shot voice conversion
approaches are not stable for unseen spe... | 2106.10406 | title_snapshot |
white21_interspeech | Optimizing an Automatic Creaky Voice Detection Method for Australian English Speaking Females | [
"Hannah White",
"Joshua Penney",
"Andy Gibson",
"Anita Szakay",
"Felicity Cox"
] | https://www.isca-archive.org/interspeech_2021/white21_interspeech.html | https://www.isca-archive.org/interspeech_2021/white21_interspeech.pdf | 10.21437/Interspeech.2021-711 | 1384-1388 | @inproceedings{white21_interspeech,
title = {{Optimizing an Automatic Creaky Voice Detection Method for Australian English Speaking Females}},
author = {Hannah White and Joshua Penney and Andy Gibson and Anita Szakay and Felicity Cox},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {... | Creaky voice is a nonmodal phonation type that has various linguistic
and sociolinguistic functions. Manually annotating creaky voice for
phonetic analysis is time-consuming and labor-intensive. In recent
years, automatic tools for detecting creaky voice have been proposed,
which present the possibility for easier, fas... | null | null |
penney21_interspeech | A Comparison of Acoustic Correlates of Voice Quality Across Different Recording Devices: A Cautionary Tale | [
"Joshua Penney",
"Andy Gibson",
"Felicity Cox",
"Michael Proctor",
"Anita Szakay"
] | https://www.isca-archive.org/interspeech_2021/penney21_interspeech.html | https://www.isca-archive.org/interspeech_2021/penney21_interspeech.pdf | 10.21437/Interspeech.2021-729 | 1389-1393 | @inproceedings{penney21_interspeech,
title = {{A Comparison of Acoustic Correlates of Voice Quality Across Different Recording Devices: A Cautionary Tale}},
author = {Joshua Penney and Andy Gibson and Felicity Cox and Michael Proctor and Anita Szakay},
year = {2021},
booktitle = {{Interspeech 2021}}... | There has been a recent increase in speech research utilizing data
recorded with participants’ personal devices, particularly in
light of the COVID-19 pandemic and restrictions on face-to-face interactions.
This raises important questions about whether these recordings are
comparable to those made in traditional lab-ba... | null | null |
sfakianaki21_interspeech | Investigating Voice Function Characteristics of Greek Speakers with Hearing Loss Using Automatic Glottal Source Feature Extraction | [
"Anna Sfakianaki",
"George P. Kafentzis"
] | https://www.isca-archive.org/interspeech_2021/sfakianaki21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sfakianaki21_interspeech.pdf | 10.21437/Interspeech.2021-870 | 1394-1398 | @inproceedings{sfakianaki21_interspeech,
title = {{Investigating Voice Function Characteristics of Greek Speakers with Hearing Loss Using Automatic Glottal Source Feature Extraction}},
author = {Anna Sfakianaki and George P. Kafentzis},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = ... | The current study investigates voice quality characteristics of Greek
adults with normal hearing and hearing loss, automatically obtained
from glottal inverse filtering analysis using the Aalto Aparat toolkit.
Aalto Aparat has been employed in glottal flow analysis of disordered
speech, but to the best of the authors’ ... | null | null |
huckvale21_interspeech | Automated Detection of Voice Disorder in the Saarbrücken Voice Database: Effects of Pathology Subset and Audio Materials | [
"Mark Huckvale",
"Catinca Buciuleac"
] | https://www.isca-archive.org/interspeech_2021/huckvale21_interspeech.html | https://www.isca-archive.org/interspeech_2021/huckvale21_interspeech.pdf | 10.21437/Interspeech.2021-1507 | 1399-1403 | @inproceedings{huckvale21_interspeech,
title = {{Automated Detection of Voice Disorder in the Saarbrücken Voice Database: Effects of Pathology Subset and Audio Materials}},
author = {Mark Huckvale and Catinca Buciuleac},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1399--1403},
... | The Saarbrücken Voice Database contains speech and simultaneous
electroglottography recordings of 1002 speakers exhibiting a wide range
of voice disorders, together with recordings of 851 controls. Previous
studies have used this database to build systems for automated detection
of voice disorders and for differential ... | null | null |
lulich21_interspeech | Accelerometer-Based Measurements of Voice Quality in Children During Semi-Occluded Vocal Tract Exercise with a Narrow Straw in Air | [
"Steven M. Lulich",
"Rita R. Patel"
] | https://www.isca-archive.org/interspeech_2021/lulich21_interspeech.html | https://www.isca-archive.org/interspeech_2021/lulich21_interspeech.pdf | 10.21437/Interspeech.2021-1918 | 1404-1408 | @inproceedings{lulich21_interspeech,
title = {{Accelerometer-Based Measurements of Voice Quality in Children During Semi-Occluded Vocal Tract Exercise with a Narrow Straw in Air}},
author = {Steven M. Lulich and Rita R. Patel},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1404--14... | Non-invasive measures of voice quality, such as H1-H2, rely on oral
flow signals, inverse filtered speech signals, or corrections for the
effects of formants. Voice quality measures play especially important
roles in the assessment of voice disorders and the evaluation of treatment
efficacy. One type of treatment that ... | null | null |
perez21_interspeech | Articulatory Coordination for Speech Motor Tracking in Huntington Disease | [
"Matthew Perez",
"Amrit Romana",
"Angela Roberts",
"Noelle Carlozzi",
"Jennifer Ann Miner",
"Praveen Dayalu",
"Emily Mower Provost"
] | https://www.isca-archive.org/interspeech_2021/perez21_interspeech.html | https://www.isca-archive.org/interspeech_2021/perez21_interspeech.pdf | 10.21437/Interspeech.2021-688 | 1409-1413 | @inproceedings{perez21_interspeech,
title = {{Articulatory Coordination for Speech Motor Tracking in Huntington Disease}},
author = {Matthew Perez and Amrit Romana and Angela Roberts and Noelle Carlozzi and Jennifer Ann Miner and Praveen Dayalu and Emily Mower Provost},
year = {2021},
booktitle = {{... | Huntington Disease (HD) is a progressive disorder which often manifests
in motor impairment. Motor severity (captured via motor score) is a
key component in assessing overall HD severity. However, motor score
evaluation involves in-clinic visits with a trained medical professional,
which are expensive and not always ac... | 2109.13815 | title_snapshot |
ferrer21_interspeech | Modeling Dysphonia Severity as a Function of Roughness and Breathiness Ratings in the GRBAS Scale | [
"Carlos A. Ferrer",
"Efren Aragón",
"María E. Hdez-Díaz",
"Marc S. de Bodt",
"Roman Cmejla",
"Marina Englert",
"Mara Behlau",
"Elmar Nöth"
] | https://www.isca-archive.org/interspeech_2021/ferrer21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ferrer21_interspeech.pdf | 10.21437/Interspeech.2021-1540 | 1414-1418 | @inproceedings{ferrer21_interspeech,
title = {{Modeling Dysphonia Severity as a Function of Roughness and Breathiness Ratings in the GRBAS Scale}},
author = {Carlos A. Ferrer and Efren Aragón and María E. Hdez-Díaz and Marc S. de Bodt and Roman Cmejla and Marina Englert and Mara Behlau and Elmar Nöth},
yea... | Dysphonia comprises many perceptually deviating aspects of voice, and
its overall severity perception is made by the listener according to
methods of aggregating the single dimensions which are personally conceived
and not well studied. Roughness and breathiness are constituent dimensions
in most devised rating scales ... | null | null |
karpov21_interspeech | Golos: Russian Dataset for Speech Research | [
"Nikolay Karpov",
"Alexander Denisenko",
"Fedor Minkin"
] | https://www.isca-archive.org/interspeech_2021/karpov21_interspeech.html | https://www.isca-archive.org/interspeech_2021/karpov21_interspeech.pdf | 10.21437/Interspeech.2021-462 | 1419-1423 | @inproceedings{karpov21_interspeech,
title = {{Golos: Russian Dataset for Speech Research}},
author = {Nikolay Karpov and Alexander Denisenko and Fedor Minkin},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1419--1423},
doi = {10.21437/Interspeech.2021-462},
issn = {... | This paper introduces a novel Russian speech dataset called Golos,
a large corpus suitable for speech research. The dataset mainly consists
of recorded audio files manually annotated on the crowd-sourcing platform.
The total duration of the audio is about 1240 hours. We have made the
corpus freely available to download... | 2106.10161 | title_snapshot |
sadhu21b_interspeech | Radically Old Way of Computing Spectra: Applications in End-to-End ASR | [
"Samik Sadhu",
"Hynek Hermansky"
] | https://www.isca-archive.org/interspeech_2021/sadhu21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/sadhu21b_interspeech.pdf | 10.21437/Interspeech.2021-643 | 1424-1428 | @inproceedings{sadhu21b_interspeech,
title = {{Radically Old Way of Computing Spectra: Applications in End-to-End ASR}},
author = {Samik Sadhu and Hynek Hermansky},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1424--1428},
doi = {10.21437/Interspeech.2021-643},
issn ... | We propose a technique to compute spectrograms using Frequency Domain
Linear Prediction (FDLP) that uses all-pole models to fit the squared
Hilbert envelope of speech in different frequency sub-bands. The spectrogram
of a complete speech utterance is computed by overlap-add of contiguous
all-pole model responses. A lon... | 2103.14129 | title_snapshot |
alghezi21_interspeech | Self-Supervised End-to-End ASR for Low Resource L2 Swedish | [
"Ragheb Al-Ghezi",
"Yaroslav Getman",
"Aku Rouhe",
"Raili Hildén",
"Mikko Kurimo"
] | https://www.isca-archive.org/interspeech_2021/alghezi21_interspeech.html | https://www.isca-archive.org/interspeech_2021/alghezi21_interspeech.pdf | 10.21437/Interspeech.2021-1710 | 1429-1433 | @inproceedings{alghezi21_interspeech,
title = {{Self-Supervised End-to-End ASR for Low Resource L2 Swedish}},
author = {Ragheb Al-Ghezi and Yaroslav Getman and Aku Rouhe and Raili Hildén and Mikko Kurimo},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1429--1433},
doi = {10... | Unlike traditional (hybrid) Automatic Speech Recognition (ASR), end-to-end
ASR systems simplify the training procedure by directly mapping acoustic
features to sequences of graphemes or characters, thereby eliminating
the need for specialized acoustic, language, or pronunciation models.
However, one drawback of end-to-... | null | null |
oneill21_interspeech | SPGISpeech: 5,000 Hours of Transcribed Financial Audio for Fully Formatted End-to-End Speech Recognition | [
"Patrick K. O’Neill",
"Vitaly Lavrukhin",
"Somshubra Majumdar",
"Vahid Noroozi",
"Yuekai Zhang",
"Oleksii Kuchaiev",
"Jagadeesh Balam",
"Yuliya Dovzhenko",
"Keenan Freyberg",
"Michael D. Shulman",
"Boris Ginsburg",
"Shinji Watanabe",
"Georg Kucsko"
] | https://www.isca-archive.org/interspeech_2021/oneill21_interspeech.html | https://www.isca-archive.org/interspeech_2021/oneill21_interspeech.pdf | 10.21437/Interspeech.2021-1860 | 1434-1438 | @inproceedings{oneill21_interspeech,
title = {{SPGISpeech: 5,000 Hours of Transcribed Financial Audio for Fully Formatted End-to-End Speech Recognition}},
author = {Patrick K. O’Neill and Vitaly Lavrukhin and Somshubra Majumdar and Vahid Noroozi and Yuekai Zhang and Oleksii Kuchaiev and Jagadeesh Balam and Y... | In the English speech-to-text (STT) machine learning task, acoustic
models are conventionally trained on uncased Latin characters, and
any necessary orthography (such as capitalization, punctuation, and
denormalization of non-standard words) is imputed by separate post-processing
models. This adds complexity and limits... | 2104.02014 | title_snapshot |
evain21_interspeech | : A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech | [
"Solène Evain",
"Ha Nguyen",
"Hang Le",
"Marcely Zanon Boito",
"Salima Mdhaffar",
"Sina Alisamir",
"Ziyi Tong",
"Natalia Tomashenko",
"Marco Dinarelli",
"Titouan Parcollet",
"Alexandre Allauzen",
"Yannick Estève",
"Benjamin Lecouteux",
"François Portet",
"Solange Rossato",
"Fabien Ring... | https://www.isca-archive.org/interspeech_2021/evain21_interspeech.html | https://www.isca-archive.org/interspeech_2021/evain21_interspeech.pdf | 10.21437/Interspeech.2021-556 | 1439-1443 | @inproceedings{evain21_interspeech,
title = {{ LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech}},
author = {Solène Evain and Ha Nguyen and Hang Le and Marcely Zanon Boito and Salima Mdhaffar and Sina Alisamir and Ziyi Tong and Natalia Tomashenko and Mar... | Self-Supervised Learning (SSL) using huge unlabeled data has been successfully
explored for image and natural language processing. Recent works also
investigated SSL from speech. They were notably successful to improve
performance on downstream tasks such as automatic speech recognition
(ASR). While these works suggest... | 2104.11462 | title_judge |
sturm21_interspeech | Prosodic Accommodation in Face-to-Face and Telephone Dialogues | [
"Pavel Šturm",
"Radek Skarnitzl",
"Tomáš Nechanský"
] | https://www.isca-archive.org/interspeech_2021/sturm21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sturm21_interspeech.pdf | 10.21437/Interspeech.2021-130 | 1444-1448 | @inproceedings{sturm21_interspeech,
title = {{Prosodic Accommodation in Face-to-Face and Telephone Dialogues}},
author = {Pavel Šturm and Radek Skarnitzl and Tomáš Nechanský},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1444--1448},
doi = {10.21437/Interspeech.2021-130},
... | The study of phonetic accommodation in various communicative situations
is still relatively limited. This paper examines accommodation in spontaneous
conversations of eight pairs of Czech young male speakers in two communicative
conditions: unconstrained face-to-face conversation and goal-oriented
interaction via mobil... | null | null |
riverincoutlee21_interspeech | Dialect Features in Heterogeneous and Homogeneous Gheg Speaking Communities | [
"Josiane Riverin-Coutlée",
"Conceição Cunha",
"Enkeleida Kapia",
"Jonathan Harrington"
] | https://www.isca-archive.org/interspeech_2021/riverincoutlee21_interspeech.html | https://www.isca-archive.org/interspeech_2021/riverincoutlee21_interspeech.pdf | 10.21437/Interspeech.2021-1090 | 1449-1453 | @inproceedings{riverincoutlee21_interspeech,
title = {{Dialect Features in Heterogeneous and Homogeneous Gheg Speaking Communities}},
author = {Josiane Riverin-Coutlée and Conceição Cunha and Enkeleida Kapia and Jonathan Harrington},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {14... | This apparent and real time study analyses how dialect features in
the speech of children and adults are differently affected depending
on whether they live in homogeneous or heterogeneous speech communities.
The general hypotheses are that speakers in such high contact settings
as heterogeneous urban centers are more ... | null | null |
zellers21_interspeech | An Exploration of the Acoustic Space of Rhotics and Laterals in Ruruuli | [
"Margaret Zellers",
"Alena Witzlack-Makarevich",
"Lilja Saeboe",
"Saudah Namyalo"
] | https://www.isca-archive.org/interspeech_2021/zellers21_interspeech.html | https://www.isca-archive.org/interspeech_2021/zellers21_interspeech.pdf | 10.21437/Interspeech.2021-1328 | 1454-1458 | @inproceedings{zellers21_interspeech,
title = {{An Exploration of the Acoustic Space of Rhotics and Laterals in Ruruuli}},
author = {Margaret Zellers and Alena Witzlack-Makarevich and Lilja Saeboe and Saudah Namyalo},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1454--1458},
doi... | Liquid consonants — rhotics and laterals — have been shown
to demonstrate unique distributional patterns cross-linguistically.
It is also claimed that rhotics are more difficult to distinguish from
one another phonetically than laterals, and that rhotics are less flexible
than laterals when it comes to participation in... | null | null |
bodur21_interspeech | Domain-Initial Strengthening in Turkish: Acoustic Cues to Prosodic Hierarchy in Stop Consonants | [
"Kubra Bodur",
"Sweeney Branje",
"Morgane Peirolo",
"Ingrid Tiscareno",
"James S. German"
] | https://www.isca-archive.org/interspeech_2021/bodur21_interspeech.html | https://www.isca-archive.org/interspeech_2021/bodur21_interspeech.pdf | 10.21437/Interspeech.2021-2230 | 1459-1463 | @inproceedings{bodur21_interspeech,
title = {{Domain-Initial Strengthening in Turkish: Acoustic Cues to Prosodic Hierarchy in Stop Consonants}},
author = {Kubra Bodur and Sweeney Branje and Morgane Peirolo and Ingrid Tiscareno and James S. German},
year = {2021},
booktitle = {{Interspeech 2021}},
... | Studies have shown that cross-linguistically, consonants at the left
edge of higher-level prosodic boundaries tend to be more forcefully
articulated than those at lower-level boundaries, a phenomenon known
as domain-initial strengthening . This study tests whether similar
effects occur in Turkish, using the Autosegment... | null | null |
zmolikova21_interspeech | Auxiliary Loss Function for Target Speech Extraction and Recognition with Weak Supervision Based on Speaker Characteristics | [
"Katerina Zmolikova",
"Marc Delcroix",
"Desh Raj",
"Shinji Watanabe",
"Jan Černocký"
] | https://www.isca-archive.org/interspeech_2021/zmolikova21_interspeech.html | https://www.isca-archive.org/interspeech_2021/zmolikova21_interspeech.pdf | 10.21437/Interspeech.2021-986 | 1464-1468 | @inproceedings{zmolikova21_interspeech,
title = {{Auxiliary Loss Function for Target Speech Extraction and Recognition with Weak Supervision Based on Speaker Characteristics}},
author = {Katerina Zmolikova and Marc Delcroix and Desh Raj and Shinji Watanabe and Jan Černocký},
year = {2021},
booktitle... | Automatic speech recognition systems deteriorate in presence of overlapped
speech. A popular approach to alleviate this is target speech extraction.
The extraction system is usually trained with a loss function measuring
the discrepancy between the estimated and the reference target speech.
This often leads to distorti... | null | null |
borsdorf21_interspeech | Universal Speaker Extraction in the Presence and Absence of Target Speakers for Speech of One and Two Talkers | [
"Marvin Borsdorf",
"Chenglin Xu",
"Haizhou Li",
"Tanja Schultz"
] | https://www.isca-archive.org/interspeech_2021/borsdorf21_interspeech.html | https://www.isca-archive.org/interspeech_2021/borsdorf21_interspeech.pdf | 10.21437/Interspeech.2021-1939 | 1469-1473 | @inproceedings{borsdorf21_interspeech,
title = {{Universal Speaker Extraction in the Presence and Absence of Target Speakers for Speech of One and Two Talkers}},
author = {Marvin Borsdorf and Chenglin Xu and Haizhou Li and Tanja Schultz},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | Speaker extraction has been studied mostly for the scenarios where
a target speaker is present in a two or more talkers mixture. Such
scenarios do not adequately reflect everyday conversations. For example,
a target speaker can be the only active talker, be quiet for a while,
or leave the conversation, that means the t... | null | null |
mateju21_interspeech | Using X-Vectors for Speech Activity Detection in Broadcast Streams | [
"Lukas Mateju",
"Frantisek Kynych",
"Petr Cerva",
"Jindrich Zdansky",
"Jiri Malek"
] | https://www.isca-archive.org/interspeech_2021/mateju21_interspeech.html | https://www.isca-archive.org/interspeech_2021/mateju21_interspeech.pdf | 10.21437/Interspeech.2021-192 | 1474-1478 | @inproceedings{mateju21_interspeech,
title = {{Using X-Vectors for Speech Activity Detection in Broadcast Streams}},
author = {Lukas Mateju and Frantisek Kynych and Petr Cerva and Jindrich Zdansky and Jiri Malek},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1474--1478},
doi ... | A new approach to speech activity detection (SAD) is presented in this
work. It allows us to reduce the complexity and computation demands,
namely in services that process streaming speech, where a SAD module
usually forms the first block of the data pipeline (e.g., in a platform
for 24/7 broadcast transcription). Our ... | null | null |
salvati21_interspeech | Time Delay Estimation for Speaker Localization Using CNN-Based Parametrized GCC-PHAT Features | [
"Daniele Salvati",
"Carlo Drioli",
"Gian Luca Foresti"
] | https://www.isca-archive.org/interspeech_2021/salvati21_interspeech.html | https://www.isca-archive.org/interspeech_2021/salvati21_interspeech.pdf | 10.21437/Interspeech.2021-988 | 1479-1483 | @inproceedings{salvati21_interspeech,
title = {{Time Delay Estimation for Speaker Localization Using CNN-Based Parametrized GCC-PHAT Features}},
author = {Daniele Salvati and Carlo Drioli and Gian Luca Foresti},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1479--1483},
doi ... | We propose a time delay estimation (TDE) method for speaker localization
based on parametrized generalized cross-correlation phase transform
(PGCC-PHAT) functions and convolutional neural networks (CNNs). The
PGCC-PHAT is used to build a feature matrix, which gives TDE information
of two microphone signals with differe... | null | null |
yousefi21_interspeech | Real-Time Speaker Counting in a Cocktail Party Scenario Using Attention-Guided Convolutional Neural Network | [
"Midia Yousefi",
"John H.L. Hansen"
] | https://www.isca-archive.org/interspeech_2021/yousefi21_interspeech.html | https://www.isca-archive.org/interspeech_2021/yousefi21_interspeech.pdf | 10.21437/Interspeech.2021-331 | 1484-1488 | @inproceedings{yousefi21_interspeech,
title = {{Real-Time Speaker Counting in a Cocktail Party Scenario Using Attention-Guided Convolutional Neural Network}},
author = {Midia Yousefi and John H.L. Hansen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1484--1488},
doi = {10... | Most current speech technology systems are designed to operate well
even in the presence of multiple active speakers. However, most solutions
assume that the number of co-current speakers is known. Unfortunately,
this information might not always be available in real-world applications.
In this study, we propose a real... | 2111.00316 | title_snapshot |
liu21d_interspeech | End-to-End Language Diarization for Bilingual Code-Switching Speech | [
"Hexin Liu",
"Leibny Paola García Perera",
"Xinyi Zhang",
"Justin Dauwels",
"Andy W.H. Khong",
"Sanjeev Khudanpur",
"Suzy J. Styles"
] | https://www.isca-archive.org/interspeech_2021/liu21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/liu21d_interspeech.pdf | 10.21437/Interspeech.2021-82 | 1489-1493 | @inproceedings{liu21d_interspeech,
title = {{End-to-End Language Diarization for Bilingual Code-Switching Speech}},
author = {Hexin Liu and Leibny Paola García Perera and Xinyi Zhang and Justin Dauwels and Andy W.H. Khong and Sanjeev Khudanpur and Suzy J. Styles},
year = {2021},
booktitle = {{Inters... | We propose two end-to-end neural configurations for language diarization
on bilingual code-switching speech. The first, a BLSTM-E2E architecture,
includes a set of stacked bidirectional LSTMs to compute embeddings
and incorporates the deep clustering loss to enforce grouping of languages
belonging to the same class. Th... | null | null |
duroselle21_interspeech | Modeling and Training Strategies for Language Recognition Systems | [
"Raphaël Duroselle",
"Md. Sahidullah",
"Denis Jouvet",
"Irina Illina"
] | https://www.isca-archive.org/interspeech_2021/duroselle21_interspeech.html | https://www.isca-archive.org/interspeech_2021/duroselle21_interspeech.pdf | 10.21437/Interspeech.2021-277 | 1494-1498 | @inproceedings{duroselle21_interspeech,
title = {{Modeling and Training Strategies for Language Recognition Systems}},
author = {Raphaël Duroselle and Md. Sahidullah and Denis Jouvet and Irina Illina},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1494--1498},
doi = {10.214... | Automatic speech recognition is complementary to language recognition.
The language recognition systems exploit this complementarity by using
frame-level bottleneck features extracted from neural networks trained
with a phone recognition task. Recent methods apply frame-level bottleneck
features extracted from an end-t... | null | null |
wang21o_interspeech | A Weight Moving Average Based Alternate Decoupled Learning Algorithm for Long-Tailed Language Identification | [
"Hui Wang",
"Lin Liu",
"Yan Song",
"Lei Fang",
"Ian McLoughlin",
"Li-Rong Dai"
] | https://www.isca-archive.org/interspeech_2021/wang21o_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21o_interspeech.pdf | 10.21437/Interspeech.2021-776 | 1499-1503 | @inproceedings{wang21o_interspeech,
title = {{A Weight Moving Average Based Alternate Decoupled Learning Algorithm for Long-Tailed Language Identification}},
author = {Hui Wang and Lin Liu and Yan Song and Lei Fang and Ian McLoughlin and Li-Rong Dai},
year = {2021},
booktitle = {{Interspeech 2021}},... | Language identification (LID) research has made tremendous progress
in recent years, especially with the introduction of deep learning
techniques. However, for real-world applications where the distribution
of different language data is highly imbalanced, the performance of
existing LID systems is still far from satisf... | null | null |
deng21b_interspeech | Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-Supervised Learning | [
"Keqi Deng",
"Songjun Cao",
"Long Ma"
] | https://www.isca-archive.org/interspeech_2021/deng21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/deng21b_interspeech.pdf | 10.21437/Interspeech.2021-1186 | 1504-1508 | @inproceedings{deng21b_interspeech,
title = {{Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-Supervised Learning}},
author = {Keqi Deng and Songjun Cao and Long Ma},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1504--1508},
doi = ... | Recently, self-supervised pre-training has gained success in automatic
speech recognition (ASR). However, considering the difference between
speech accents in real scenarios, how to identify accents and use accent
features to improve ASR is still challenging. In this paper, we employ
the self-supervised pre-training me... | 2109.07349 | title_snapshot |
fan21_interspeech | Exploring wav2vec 2.0 on Speaker Verification and Language Identification | [
"Zhiyun Fan",
"Meng Li",
"Shiyu Zhou",
"Bo Xu"
] | https://www.isca-archive.org/interspeech_2021/fan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/fan21_interspeech.pdf | 10.21437/Interspeech.2021-1280 | 1509-1513 | @inproceedings{fan21_interspeech,
title = {{Exploring wav2vec 2.0 on Speaker Verification and Language Identification}},
author = {Zhiyun Fan and Meng Li and Shiyu Zhou and Bo Xu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {1509--1513},
doi = {10.21437/Interspeech.2021-1... | wav2vec 2.0 is a recently proposed self-supervised framework for speech
representation learning. It follows a two-stage training process of
pre-training and fine-tuning, and performs well in speech recognition
tasks especially ultra-low resource cases. In this work, we attempt
to extend the self-supervised framework to... | 2012.06185 | title_snapshot |
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