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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|---|---|---|---|---|---|---|---|---|---|---|
ali21_interspeech | Group Delay Based Re-Weighted Sparse Recovery Algorithms for Robust and High-Resolution Source Separation in DOA Framework | [
"Murtiza Ali",
"Ashwani Koul",
"Karan Nathwani"
] | https://www.isca-archive.org/interspeech_2021/ali21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ali21_interspeech.pdf | 10.21437/Interspeech.2021-164 | 3031-3035 | @inproceedings{ali21_interspeech,
title = {{Group Delay Based Re-Weighted Sparse Recovery Algorithms for Robust and High-Resolution Source Separation in DOA Framework}},
author = {Murtiza Ali and Ashwani Koul and Karan Nathwani},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3031--... | Sparse Recovery (SR) algorithms have been used widely for direction-of-arrival
(DOA) estimation in spatially contiguous plane wave for their robust
performance. But these algorithms have proven to be computationally
costly. With a few sensors and at low SNRs, the noise dominates the
data singular vectors and the sparse... | null | null |
han21d_interspeech | Continuous Speech Separation Using Speaker Inventory for Long Recording | [
"Cong Han",
"Yi Luo",
"Chenda Li",
"Tianyan Zhou",
"Keisuke Kinoshita",
"Shinji Watanabe",
"Marc Delcroix",
"Hakan Erdogan",
"John R. Hershey",
"Nima Mesgarani",
"Zhuo Chen"
] | https://www.isca-archive.org/interspeech_2021/han21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/han21d_interspeech.pdf | 10.21437/Interspeech.2021-338 | 3036-3040 | @inproceedings{han21d_interspeech,
title = {{Continuous Speech Separation Using Speaker Inventory for Long Recording}},
author = {Cong Han and Yi Luo and Chenda Li and Tianyan Zhou and Keisuke Kinoshita and Shinji Watanabe and Marc Delcroix and Hakan Erdogan and John R. Hershey and Nima Mesgarani and Zhuo Ch... | Leveraging additional speaker information to facilitate speech separation
has received increasing attention in recent years. Recent research
includes extracting target speech by using the target speaker’s
voice snippet and jointly separating all participating speakers by
using a pool of additional speaker signals, whic... | 2012.09727 | title_judge |
yuan21_interspeech | Crossfire Conditional Generative Adversarial Networks for Singing Voice Extraction | [
"Weitao Yuan",
"Shengbei Wang",
"Xiangrui Li",
"Masashi Unoki",
"Wenwu Wang"
] | https://www.isca-archive.org/interspeech_2021/yuan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/yuan21_interspeech.pdf | 10.21437/Interspeech.2021-433 | 3041-3045 | @inproceedings{yuan21_interspeech,
title = {{Crossfire Conditional Generative Adversarial Networks for Singing Voice Extraction}},
author = {Weitao Yuan and Shengbei Wang and Xiangrui Li and Masashi Unoki and Wenwu Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3041--3045},
... | Generative adversarial networks (GANs) and Conditional GANs (cGANs)
have recently been applied for singing voice extraction (SVE), since
they can accurately model the vocal distributions and effectively utilize
a large amount of unlabelled datasets. However, current GANs/cGANs
based SVE frameworks have no explicit mech... | null | null |
wang21w_interspeech | End-to-End Speech Separation Using Orthogonal Representation in Complex and Real Time-Frequency Domain | [
"Kai Wang",
"Hao Huang",
"Ying Hu",
"Zhihua Huang",
"Sheng Li"
] | https://www.isca-archive.org/interspeech_2021/wang21w_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21w_interspeech.pdf | 10.21437/Interspeech.2021-504 | 3046-3050 | @inproceedings{wang21w_interspeech,
title = {{End-to-End Speech Separation Using Orthogonal Representation in Complex and Real Time-Frequency Domain}},
author = {Kai Wang and Hao Huang and Ying Hu and Zhihua Huang and Sheng Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3046--3... | Traditional single channel speech separation in the time-frequency
(T-F) domain often faces the problem of phase reconstruction. Due to
the fact that the real-valued network is not suitable for dealing with
complex-valued representation, the performance of the T-F domain speech
separation method is often constrained fr... | null | null |
nakagome21_interspeech | Efficient and Stable Adversarial Learning Using Unpaired Data for Unsupervised Multichannel Speech Separation | [
"Yu Nakagome",
"Masahito Togami",
"Tetsuji Ogawa",
"Tetsunori Kobayashi"
] | https://www.isca-archive.org/interspeech_2021/nakagome21_interspeech.html | https://www.isca-archive.org/interspeech_2021/nakagome21_interspeech.pdf | 10.21437/Interspeech.2021-523 | 3051-3055 | @inproceedings{nakagome21_interspeech,
title = {{Efficient and Stable Adversarial Learning Using Unpaired Data for Unsupervised Multichannel Speech Separation}},
author = {Yu Nakagome and Masahito Togami and Tetsuji Ogawa and Tetsunori Kobayashi},
year = {2021},
booktitle = {{Interspeech 2021}},
p... | This study presents a framework to enable efficient and stable adversarial
learning of unsupervised multichannel source separation models. When
the paired data, i.e., the mixture and the corresponding clean speech,
are not available for training, it is promising to exploit generative
adversarial networks (GANs), where ... | null | null |
huang21h_interspeech | Stabilizing Label Assignment for Speech Separation by Self-Supervised Pre-Training | [
"Sung-Feng Huang",
"Shun-Po Chuang",
"Da-Rong Liu",
"Yi-Chen Chen",
"Gene-Ping Yang",
"Hung-yi Lee"
] | https://www.isca-archive.org/interspeech_2021/huang21h_interspeech.html | https://www.isca-archive.org/interspeech_2021/huang21h_interspeech.pdf | 10.21437/Interspeech.2021-763 | 3056-3060 | @inproceedings{huang21h_interspeech,
title = {{Stabilizing Label Assignment for Speech Separation by Self-Supervised Pre-Training}},
author = {Sung-Feng Huang and Shun-Po Chuang and Da-Rong Liu and Yi-Chen Chen and Gene-Ping Yang and Hung-yi Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
p... | Speech separation has been well developed, with the very successful
permutation invariant training (PIT) approach, although the frequent
label assignment switching happening during PIT training remains to
be a problem when better convergence speed and achievable performance
are desired. In this paper, we propose to per... | 2010.15366 | title_snapshot |
wang21x_interspeech | Dual-Path Filter Network: Speaker-Aware Modeling for Speech Separation | [
"Fan-Lin Wang",
"Yu-Huai Peng",
"Hung-Shin Lee",
"Hsin-Min Wang"
] | https://www.isca-archive.org/interspeech_2021/wang21x_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21x_interspeech.pdf | 10.21437/Interspeech.2021-858 | 3061-3065 | @inproceedings{wang21x_interspeech,
title = {{Dual-Path Filter Network: Speaker-Aware Modeling for Speech Separation}},
author = {Fan-Lin Wang and Yu-Huai Peng and Hung-Shin Lee and Hsin-Min Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3061--3065},
doi = {10.21437/I... | Speech separation has been extensively studied to deal with the cocktail
party problem in recent years. All related approaches can be divided
into two categories: time-frequency domain methods and time domain
methods. In addition, some methods try to generate speaker vectors
to support source separation. In this study,... | 2106.07579 | title_snapshot |
wu21f_interspeech | Investigation of Practical Aspects of Single Channel Speech Separation for ASR | [
"Jian Wu",
"Zhuo Chen",
"Sanyuan Chen",
"Yu Wu",
"Takuya Yoshioka",
"Naoyuki Kanda",
"Shujie Liu",
"Jinyu Li"
] | https://www.isca-archive.org/interspeech_2021/wu21f_interspeech.html | https://www.isca-archive.org/interspeech_2021/wu21f_interspeech.pdf | 10.21437/Interspeech.2021-921 | 3066-3070 | @inproceedings{wu21f_interspeech,
title = {{Investigation of Practical Aspects of Single Channel Speech Separation for ASR}},
author = {Jian Wu and Zhuo Chen and Sanyuan Chen and Yu Wu and Takuya Yoshioka and Naoyuki Kanda and Shujie Liu and Jinyu Li},
year = {2021},
booktitle = {{Interspeech 2021}}... | Speech separation has been successfully applied as a front-end processing
module of conversation transcription systems thanks to its ability
to handle overlapped speech and its flexibility to combine with downstream
tasks such as automatic speech recognition (ASR). However, a speech
separation model often introduces ta... | 2107.01922 | title_snapshot |
luo21c_interspeech | Implicit Filter-and-Sum Network for End-to-End Multi-Channel Speech Separation | [
"Yi Luo",
"Nima Mesgarani"
] | https://www.isca-archive.org/interspeech_2021/luo21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/luo21c_interspeech.pdf | 10.21437/Interspeech.2021-1158 | 3071-3075 | @inproceedings{luo21c_interspeech,
title = {{Implicit Filter-and-Sum Network for End-to-End Multi-Channel Speech Separation}},
author = {Yi Luo and Nima Mesgarani},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3071--3075},
doi = {10.21437/Interspeech.2021-1158},
issn ... | Various neural network architectures have been proposed in recent years
for the task of multi-channel speech separation. Among them, the filter-and-sum
network (FaSNet) performs end-to-end time-domain filter-and-sum beamforming
and has shown effective in both ad-hoc and fixed microphone array geometries.
However, wheth... | 2011.08401 | title_judge |
xu21i_interspeech | Generalized Spatio-Temporal RNN Beamformer for Target Speech Separation | [
"Yong Xu",
"Zhuohuang Zhang",
"Meng Yu",
"Shi-Xiong Zhang",
"Dong Yu"
] | https://www.isca-archive.org/interspeech_2021/xu21i_interspeech.html | https://www.isca-archive.org/interspeech_2021/xu21i_interspeech.pdf | 10.21437/Interspeech.2021-430 | 3076-3080 | @inproceedings{xu21i_interspeech,
title = {{Generalized Spatio-Temporal RNN Beamformer for Target Speech Separation}},
author = {Yong Xu and Zhuohuang Zhang and Meng Yu and Shi-Xiong Zhang and Dong Yu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3076--3080},
doi = {10.21... | Although the conventional mask-based minimum variance distortionless
response (MVDR) could reduce the non-linear distortion, the residual
noise level of the MVDR separated speech is still high. In this paper,
we propose a spatio-temporal recurrent neural network based beamformer
(RNN-BF) for target speech separation. T... | 2101.01280 | title_snapshot |
liu21j_interspeech | End-to-End Neural Diarization: From Transformer to Conformer | [
"Yi Chieh Liu",
"Eunjung Han",
"Chul Lee",
"Andreas Stolcke"
] | https://www.isca-archive.org/interspeech_2021/liu21j_interspeech.html | https://www.isca-archive.org/interspeech_2021/liu21j_interspeech.pdf | 10.21437/Interspeech.2021-1909 | 3081-3085 | @inproceedings{liu21j_interspeech,
title = {{End-to-End Neural Diarization: From Transformer to Conformer}},
author = {Yi Chieh Liu and Eunjung Han and Chul Lee and Andreas Stolcke},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3081--3085},
doi = {10.21437/Interspeech.2021... | We propose a new end-to-end neural diarization (EEND) system that is
based on Conformer, a recently proposed neural architecture that combines
convolutional mappings and Transformer to model both local and global
dependencies in speech. We first show that data augmentation and convolutional
subsampling layers enhance t... | 2106.07167 | title_snapshot |
jung21_interspeech | Three-Class Overlapped Speech Detection Using a Convolutional Recurrent Neural Network | [
"Jee-weon Jung",
"Hee-Soo Heo",
"Youngki Kwon",
"Joon Son Chung",
"Bong-Jin Lee"
] | https://www.isca-archive.org/interspeech_2021/jung21_interspeech.html | https://www.isca-archive.org/interspeech_2021/jung21_interspeech.pdf | 10.21437/Interspeech.2021-149 | 3086-3090 | @inproceedings{jung21_interspeech,
title = {{Three-Class Overlapped Speech Detection Using a Convolutional Recurrent Neural Network}},
author = {Jee-weon Jung and Hee-Soo Heo and Youngki Kwon and Joon Son Chung and Bong-Jin Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3086--... | In this work, we propose an overlapped speech detection system trained
as a three-class classifier. Unlike conventional systems that perform
binary classification as to whether or not a frame contains overlapped
speech, the proposed approach classifies into three classes: non-speech,
single speaker speech, and overlapp... | 2104.02878 | title_snapshot |
wan21_interspeech | Online Speaker Diarization Equipped with Discriminative Modeling and Guided Inference | [
"Xucheng Wan",
"Kai Liu",
"Huan Zhou"
] | https://www.isca-archive.org/interspeech_2021/wan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/wan21_interspeech.pdf | 10.21437/Interspeech.2021-261 | 3091-3095 | @inproceedings{wan21_interspeech,
title = {{Online Speaker Diarization Equipped with Discriminative Modeling and Guided Inference}},
author = {Xucheng Wan and Kai Liu and Huan Zhou},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3091--3095},
doi = {10.21437/Interspeech.2021... | Despite considerable efforts, online speaker diarization remains an
ongoing challenge. In this study, we propose to tackle the challenge
from two perspectives, to endow diarization model with discriminability
and to rectify less-reliable online inference with guidance. Specifically,
based on the current prior art, UIS-... | null | null |
takashima21_interspeech | Semi-Supervised Training with Pseudo-Labeling for End-To-End Neural Diarization | [
"Yuki Takashima",
"Yusuke Fujita",
"Shota Horiguchi",
"Shinji Watanabe",
"Leibny Paola García Perera",
"Kenji Nagamatsu"
] | https://www.isca-archive.org/interspeech_2021/takashima21_interspeech.html | https://www.isca-archive.org/interspeech_2021/takashima21_interspeech.pdf | 10.21437/Interspeech.2021-384 | 3096-3100 | @inproceedings{takashima21_interspeech,
title = {{Semi-Supervised Training with Pseudo-Labeling for End-To-End Neural Diarization}},
author = {Yuki Takashima and Yusuke Fujita and Shota Horiguchi and Shinji Watanabe and Leibny Paola García Perera and Kenji Nagamatsu},
year = {2021},
booktitle = {{In... | In this paper, we present a semi-supervised training technique using
pseudo-labeling for end-to-end neural diarization (EEND). The EEND
system has shown promising performance compared with traditional clustering-based
methods, especially in the case of overlapping speech. However, to
get a well-tuned model, EEND requir... | 2106.04764 | title_snapshot |
kwon21b_interspeech | Adapting Speaker Embeddings for Speaker Diarisation | [
"Youngki Kwon",
"Jee-weon Jung",
"Hee-Soo Heo",
"You Jin Kim",
"Bong-Jin Lee",
"Joon Son Chung"
] | https://www.isca-archive.org/interspeech_2021/kwon21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/kwon21b_interspeech.pdf | 10.21437/Interspeech.2021-448 | 3101-3105 | @inproceedings{kwon21b_interspeech,
title = {{Adapting Speaker Embeddings for Speaker Diarisation}},
author = {Youngki Kwon and Jee-weon Jung and Hee-Soo Heo and You Jin Kim and Bong-Jin Lee and Joon Son Chung},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3101--3105},
doi ... | The goal of this paper is to adapt speaker embeddings for solving the
problem of speaker diarisation. The quality of speaker embeddings is
paramount to the performance of speaker diarisation systems. Despite
this, prior works in the field have directly used embeddings designed
only to be effective on the speaker verifi... | 2104.02879 | title_snapshot |
wang21y_interspeech | Scenario-Dependent Speaker Diarization for DIHARD-III Challenge | [
"Yu-Xuan Wang",
"Jun Du",
"Maokui He",
"Shu-Tong Niu",
"Lei Sun",
"Chin-Hui Lee"
] | https://www.isca-archive.org/interspeech_2021/wang21y_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21y_interspeech.pdf | 10.21437/Interspeech.2021-516 | 3106-3110 | @inproceedings{wang21y_interspeech,
title = {{Scenario-Dependent Speaker Diarization for DIHARD-III Challenge}},
author = {Yu-Xuan Wang and Jun Du and Maokui He and Shu-Tong Niu and Lei Sun and Chin-Hui Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3106--3110},
doi = ... | In this study, we propose a scenario-dependent speaker diarization
approach to handling the diversified scenarios of 11 domains encountered
in DIHARD-III challenge with a divide-and-conquer strategy. First,
using a ResNet-based audio domain classifier, all domains in DIHARD-III
challenge could be divided into several s... | null | null |
bredin21_interspeech | End-To-End Speaker Segmentation for Overlap-Aware Resegmentation | [
"Hervé Bredin",
"Antoine Laurent"
] | https://www.isca-archive.org/interspeech_2021/bredin21_interspeech.html | https://www.isca-archive.org/interspeech_2021/bredin21_interspeech.pdf | 10.21437/Interspeech.2021-560 | 3111-3115 | @inproceedings{bredin21_interspeech,
title = {{End-To-End Speaker Segmentation for Overlap-Aware Resegmentation}},
author = {Hervé Bredin and Antoine Laurent},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3111--3115},
doi = {10.21437/Interspeech.2021-560},
issn = {2... | Speaker segmentation consists in partitioning a conversation between
one or more speakers into speaker turns. Usually addressed as the late
combination of three sub-tasks (voice activity detection, speaker change
detection, and overlapped speech detection), we propose to train an
end-to-end segmentation model that does... | 2104.04045 | title_snapshot |
xue21d_interspeech | Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers | [
"Yawen Xue",
"Shota Horiguchi",
"Yusuke Fujita",
"Yuki Takashima",
"Shinji Watanabe",
"Leibny Paola García Perera",
"Kenji Nagamatsu"
] | https://www.isca-archive.org/interspeech_2021/xue21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/xue21d_interspeech.pdf | 10.21437/Interspeech.2021-708 | 3116-3120 | @inproceedings{xue21d_interspeech,
title = {{Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers}},
author = {Yawen Xue and Shota Horiguchi and Yusuke Fujita and Yuki Takashima and Shinji Watanabe and Leibny Paola García Perera and Kenji Nagamatsu},
y... | We propose a streaming diarization method based on an end-to-end neural
diarization (EEND) model, which handles flexible numbers of speakers
and overlapping speech. In our previous study, the speaker-tracing
buffer (STB) mechanism was proposed to achieve a chunk-wise streaming
diarization using a pre-trained EEND model... | 2101.08473 | title_snapshot |
anidjar21_interspeech | A Thousand Words are Worth More Than One Recording: Based Speaker Change Detection | [
"Or Haim Anidjar",
"Itshak Lapidot",
"Chen Hajaj",
"Amit Dvir"
] | https://www.isca-archive.org/interspeech_2021/anidjar21_interspeech.html | https://www.isca-archive.org/interspeech_2021/anidjar21_interspeech.pdf | 10.21437/Interspeech.2021-87 | 3121-3125 | @inproceedings{anidjar21_interspeech,
title = {{A Thousand Words are Worth More Than One Recording: Word-Embedding Based Speaker Change Detection}},
author = {Or Haim Anidjar and Itshak Lapidot and Chen Hajaj and Amit Dvir},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3121--3125}... | Speaker Change Detection (SCD) is the task of segmenting an input audio-recording
according to speaker interchanges. This task is essential for many
applications, such as automatic voice transcription or Speaker Diarization
(SD). This paper focuses on the essential task of audio segmentation
and suggests a word-embeddi... | 2006.01206 | title_judge |
futamata21_interspeech | Phrase Break Prediction with Bidirectional Encoder Representations in Japanese Text-to-Speech Synthesis | [
"Kosuke Futamata",
"Byeongseon Park",
"Ryuichi Yamamoto",
"Kentaro Tachibana"
] | https://www.isca-archive.org/interspeech_2021/futamata21_interspeech.html | https://www.isca-archive.org/interspeech_2021/futamata21_interspeech.pdf | 10.21437/Interspeech.2021-252 | 3126-3130 | @inproceedings{futamata21_interspeech,
title = {{Phrase Break Prediction with Bidirectional Encoder Representations in Japanese Text-to-Speech Synthesis}},
author = {Kosuke Futamata and Byeongseon Park and Ryuichi Yamamoto and Kentaro Tachibana},
year = {2021},
booktitle = {{Interspeech 2021}},
pa... | We propose a novel phrase break prediction method that combines implicit
features extracted from a pre-trained large language model, a.k.a BERT,
and explicit features extracted from BiLSTM with linguistic features.
In conventional BiLSTM-based methods, word representations and/or sentence
representations are used as in... | 2104.12395 | title_snapshot |
vallesperez21_interspeech | Improving Multi-Speaker TTS Prosody Variance with a Residual Encoder and Normalizing Flows | [
"Iván Vallés-Pérez",
"Julian Roth",
"Grzegorz Beringer",
"Roberto Barra-Chicote",
"Jasha Droppo"
] | https://www.isca-archive.org/interspeech_2021/vallesperez21_interspeech.html | https://www.isca-archive.org/interspeech_2021/vallesperez21_interspeech.pdf | 10.21437/Interspeech.2021-562 | 3131-3135 | @inproceedings{vallesperez21_interspeech,
title = {{Improving Multi-Speaker TTS Prosody Variance with a Residual Encoder and Normalizing Flows}},
author = {Iván Vallés-Pérez and Julian Roth and Grzegorz Beringer and Roberto Barra-Chicote and Jasha Droppo},
year = {2021},
booktitle = {{Interspeech 20... | Text-to-speech systems recently achieved almost indistinguishable quality
from human speech. However, the prosody of those systems is generally
flatter than natural speech, producing samples with low expressiveness.
Disentanglement of speaker id and prosody is crucial in text-to-speech
systems to improve on naturalness... | 2106.05762 | title_snapshot |
du21b_interspeech | Rich Prosody Diversity Modelling with Phone-Level Mixture Density Network | [
"Chenpeng Du",
"Kai Yu"
] | https://www.isca-archive.org/interspeech_2021/du21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/du21b_interspeech.pdf | 10.21437/Interspeech.2021-802 | 3136-3140 | @inproceedings{du21b_interspeech,
title = {{Rich Prosody Diversity Modelling with Phone-Level Mixture Density Network}},
author = {Chenpeng Du and Kai Yu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3136--3140},
doi = {10.21437/Interspeech.2021-802},
issn = {2958-... | Generating natural speech with a diverse and smooth prosody pattern
is a challenging task. Although random sampling with phone-level prosody
distribution has been investigated to generate different prosody patterns,
the diversity of the generated speech is still very limited and far
from what can be achieved by humans.... | 2102.00851 | title_snapshot |
fujita21_interspeech | Phoneme Duration Modeling Using Speech Rhythm-Based Speaker Embeddings for Multi-Speaker Speech Synthesis | [
"Kenichi Fujita",
"Atsushi Ando",
"Yusuke Ijima"
] | https://www.isca-archive.org/interspeech_2021/fujita21_interspeech.html | https://www.isca-archive.org/interspeech_2021/fujita21_interspeech.pdf | 10.21437/Interspeech.2021-826 | 3141-3145 | @inproceedings{fujita21_interspeech,
title = {{Phoneme Duration Modeling Using Speech Rhythm-Based Speaker Embeddings for Multi-Speaker Speech Synthesis}},
author = {Kenichi Fujita and Atsushi Ando and Yusuke Ijima},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3141--3145},
doi ... | This paper proposes a novel speech-rhythm-based method for speaker
embeddings. Conventionally spectral feature-based speaker embedding
vectors such as the x-vector are used as auxiliary information for
multi-speaker speech synthesis. However, speech synthesis with conventional
embeddings has difficulty reproducing the ... | null | null |
zou21_interspeech | Fine-Grained Prosody Modeling in Neural Speech Synthesis Using ToBI Representation | [
"Yuxiang Zou",
"Shichao Liu",
"Xiang Yin",
"Haopeng Lin",
"Chunfeng Wang",
"Haoyu Zhang",
"Zejun Ma"
] | https://www.isca-archive.org/interspeech_2021/zou21_interspeech.html | https://www.isca-archive.org/interspeech_2021/zou21_interspeech.pdf | 10.21437/Interspeech.2021-883 | 3146-3150 | @inproceedings{zou21_interspeech,
title = {{Fine-Grained Prosody Modeling in Neural Speech Synthesis Using ToBI Representation}},
author = {Yuxiang Zou and Shichao Liu and Xiang Yin and Haopeng Lin and Chunfeng Wang and Haoyu Zhang and Zejun Ma},
year = {2021},
booktitle = {{Interspeech 2021}},
pa... | Benefiting from the great development of deep learning, modern neural
text-to-speech (TTS) models can generate speech indistinguishable from
natural speech. However, The generated utterances often keep an average
prosodic style of the database instead of having rich prosodic variation.
For pitch-stressed languages, suc... | null | null |
sharma21b_interspeech | Intra-Sentential Speaking Rate Control in Neural Text-To-Speech for Automatic Dubbing | [
"Mayank Sharma",
"Yogesh Virkar",
"Marcello Federico",
"Roberto Barra-Chicote",
"Robert Enyedi"
] | https://www.isca-archive.org/interspeech_2021/sharma21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/sharma21b_interspeech.pdf | 10.21437/Interspeech.2021-1012 | 3151-3155 | @inproceedings{sharma21b_interspeech,
title = {{Intra-Sentential Speaking Rate Control in Neural Text-To-Speech for Automatic Dubbing}},
author = {Mayank Sharma and Yogesh Virkar and Marcello Federico and Roberto Barra-Chicote and Robert Enyedi},
year = {2021},
booktitle = {{Interspeech 2021}},
pa... | Automatically dubbed speech of a video involves: (i) segmenting the
target sentences into phrases to reflect the speech-pause arrangement
used by the original speaker, and (ii) adjusting the speaking rate
of the synthetic voice at the phrase-level to match the exact timing
of each corresponding source phrase. In this w... | null | null |
zhang21u_interspeech | Applying the Information Bottleneck Principle to Prosodic Representation Learning | [
"Guangyan Zhang",
"Ying Qin",
"Daxin Tan",
"Tan Lee"
] | https://www.isca-archive.org/interspeech_2021/zhang21u_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21u_interspeech.pdf | 10.21437/Interspeech.2021-1049 | 3156-3160 | @inproceedings{zhang21u_interspeech,
title = {{Applying the Information Bottleneck Principle to Prosodic Representation Learning}},
author = {Guangyan Zhang and Ying Qin and Daxin Tan and Tan Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3156--3160},
doi = {10.21437/I... | This paper describes a novel design of a neural network-based speech
generation model for learning prosodic representation. The problem
of representation learning is formulated according to the information
bottleneck (IB) principle. A modified VQ-VAE quantized layer is incorporated
in the speech generation model to con... | 2108.02821 | title_snapshot |
baird21_interspeech | A Prototypical Network Approach for Evaluating Generated Emotional Speech | [
"Alice Baird",
"Silvan Mertes",
"Manuel Milling",
"Lukas Stappen",
"Thomas Wiest",
"Elisabeth André",
"Björn W. Schuller"
] | https://www.isca-archive.org/interspeech_2021/baird21_interspeech.html | https://www.isca-archive.org/interspeech_2021/baird21_interspeech.pdf | 10.21437/Interspeech.2021-1123 | 3161-3165 | @inproceedings{baird21_interspeech,
title = {{A Prototypical Network Approach for Evaluating Generated Emotional Speech}},
author = {Alice Baird and Silvan Mertes and Manuel Milling and Lukas Stappen and Thomas Wiest and Elisabeth André and Björn W. Schuller},
year = {2021},
booktitle = {{Interspeec... | The collection of emotional speech data is a time-consuming and costly
endeavour. Generative networks can be applied to augment the limited
audio data artificially. However, it is challenging to evaluate generated
audio for its similarity to source data, as current quantitative metrics
are not necessarily suited to the... | null | null |
yoshinaga21_interspeech | A Simplified Model for the Vocal Tract of [s] with Inclined Incisors | [
"Tsukasa Yoshinaga",
"Kohei Tada",
"Kazunori Nozaki",
"Akiyoshi Iida"
] | https://www.isca-archive.org/interspeech_2021/yoshinaga21_interspeech.html | https://www.isca-archive.org/interspeech_2021/yoshinaga21_interspeech.pdf | 10.21437/Interspeech.2021-231 | 3166-3170 | @inproceedings{yoshinaga21_interspeech,
title = {{A Simplified Model for the Vocal Tract of [s] with Inclined Incisors}},
author = {Tsukasa Yoshinaga and Kohei Tada and Kazunori Nozaki and Akiyoshi Iida},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3166--3170},
doi = {10.... | To examine the effects of inclined incisors on the phonation of [s],
a simplified vocal tract model is proposed, and the acoustic characteristics
with different maxillary incisor angles are predicted by the model.
As a control model, a realistic vocal tract replica of [s] was constructed
from medical images, and the an... | null | null |
arai21b_interspeech | Vocal-Tract Models to Visualize the Airstream of Human Breath and Droplets While Producing Speech | [
"Takayuki Arai"
] | https://www.isca-archive.org/interspeech_2021/arai21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/arai21b_interspeech.pdf | 10.21437/Interspeech.2021-449 | 3171-3175 | @inproceedings{arai21b_interspeech,
title = {{Vocal-Tract Models to Visualize the Airstream of Human Breath and Droplets While Producing Speech}},
author = {Takayuki Arai},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3171--3175},
doi = {10.21437/Interspeech.2021-449},
i... | Due to the COVID-19 pandemic, visualizing the airstream of human breath
during speech production has become extremely important from the viewpoint
of preventing infection. In addition, visualizing droplets and the
larger drops expelled when we speak consonantal sounds may help for
the same reason. One visualization tec... | null | null |
tanji21_interspeech | Using Transposed Convolution for Articulatory-to-Acoustic Conversion from Real-Time MRI Data | [
"Ryo Tanji",
"Hidefumi Ohmura",
"Kouichi Katsurada"
] | https://www.isca-archive.org/interspeech_2021/tanji21_interspeech.html | https://www.isca-archive.org/interspeech_2021/tanji21_interspeech.pdf | 10.21437/Interspeech.2021-906 | 3176-3180 | @inproceedings{tanji21_interspeech,
title = {{Using Transposed Convolution for Articulatory-to-Acoustic Conversion from Real-Time MRI Data}},
author = {Ryo Tanji and Hidefumi Ohmura and Kouichi Katsurada},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3176--3180},
doi = {10... | We herein propose a deep neural network-based model for articulatory-to-acoustic
conversion from real-time MRI data. Although rtMRI, which can record
entire articulatory organs with a high resolution, has an advantage
in articulatory-to-acoustic conversion, it has a relatively low sampling
rate. To address this, we inc... | null | null |
inaam21_interspeech | Comparison Between Lumped-Mass Modeling and Flow Simulation of the Reed-Type Artificial Vocal Fold | [
"Rafia Inaam",
"Tsukasa Yoshinaga",
"Takayuki Arai",
"Hiroshi Yokoyama",
"Akiyoshi Iida"
] | https://www.isca-archive.org/interspeech_2021/inaam21_interspeech.html | https://www.isca-archive.org/interspeech_2021/inaam21_interspeech.pdf | 10.21437/Interspeech.2021-929 | 3181-3185 | @inproceedings{inaam21_interspeech,
title = {{Comparison Between Lumped-Mass Modeling and Flow Simulation of the Reed-Type Artificial Vocal Fold}},
author = {Rafia Inaam and Tsukasa Yoshinaga and Takayuki Arai and Hiroshi Yokoyama and Akiyoshi Iida},
year = {2021},
booktitle = {{Interspeech 2021}},
... | The sound generated by a reed-type artificial vocal fold was predicted
by a one-mass modeling and numerical flow simulation to examine the
sound generation mechanisms of the artificial vocal fold. For the one-mass
modeling, the reed oscillation was modeled with an equivalent spring
constant, and the flow rate was estim... | null | null |
werner21_interspeech | Inhalations in Speech: Acoustic and Physiological Characteristics | [
"Raphael Werner",
"Susanne Fuchs",
"Jürgen Trouvain",
"Bernd Möbius"
] | https://www.isca-archive.org/interspeech_2021/werner21_interspeech.html | https://www.isca-archive.org/interspeech_2021/werner21_interspeech.pdf | 10.21437/Interspeech.2021-1262 | 3186-3190 | @inproceedings{werner21_interspeech,
title = {{Inhalations in Speech: Acoustic and Physiological Characteristics}},
author = {Raphael Werner and Susanne Fuchs and Jürgen Trouvain and Bernd Möbius},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3186--3190},
doi = {10.21437/I... | This paper examines the acoustic properties of breath noises in speech
pauses in relation to similar speech segments and with regard to their
inhalation speed. We measured intensity, center of gravity, and formants,
as well as kinematic data (via Respiratory Inductance Plethysmography)
for inhalations, aspirations of s... | null | null |
xu21j_interspeech | Model-Based Exploration of Linking Between Vowel Articulatory Space and Acoustic Space | [
"Anqi Xu",
"Daniel van Niekerk",
"Branislav Gerazov",
"Paul Konstantin Krug",
"Santitham Prom-on",
"Peter Birkholz",
"Yi Xu"
] | https://www.isca-archive.org/interspeech_2021/xu21j_interspeech.html | https://www.isca-archive.org/interspeech_2021/xu21j_interspeech.pdf | 10.21437/Interspeech.2021-1422 | 3191-3195 | @inproceedings{xu21j_interspeech,
title = {{Model-Based Exploration of Linking Between Vowel Articulatory Space and Acoustic Space}},
author = {Anqi Xu and Daniel van Niekerk and Branislav Gerazov and Paul Konstantin Krug and Santitham Prom-on and Peter Birkholz and Yi Xu},
year = {2021},
booktitle ... | While the acoustic vowel space has been extensively studied in previous
research, little is known about the high-dimensional articulatory space
of vowels. The articulatory imaging techniques are limited to tracking
only a few key articulators, leaving the rest of the articulators unmonitored.
In the present study, we a... | null | null |
elmers21_interspeech | Take a Breath: Respiratory Sounds Improve Recollection in Synthetic Speech | [
"Mikey Elmers",
"Raphael Werner",
"Beeke Muhlack",
"Bernd Möbius",
"Jürgen Trouvain"
] | https://www.isca-archive.org/interspeech_2021/elmers21_interspeech.html | https://www.isca-archive.org/interspeech_2021/elmers21_interspeech.pdf | 10.21437/Interspeech.2021-1496 | 3196-3200 | @inproceedings{elmers21_interspeech,
title = {{Take a Breath: Respiratory Sounds Improve Recollection in Synthetic Speech}},
author = {Mikey Elmers and Raphael Werner and Beeke Muhlack and Bernd Möbius and Jürgen Trouvain},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3196--3200},... | This study revisits Whalen et al. (1995, JASA) by evaluating English
speaking participants in a perception experiment to determine if their
recollection is affected by including breath noises in sentences generated
by a speech synthesis system. Whalen found an improvement in recollection
for sentences that were precede... | null | null |
chen21m_interspeech | Modeling Sensorimotor Adaptation in Speech Through Alterations to Forward and Inverse Models | [
"Taijing Chen",
"Adam Lammert",
"Benjamin Parrell"
] | https://www.isca-archive.org/interspeech_2021/chen21m_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21m_interspeech.pdf | 10.21437/Interspeech.2021-1746 | 3201-3205 | @inproceedings{chen21m_interspeech,
title = {{Modeling Sensorimotor Adaptation in Speech Through Alterations to Forward and Inverse Models}},
author = {Taijing Chen and Adam Lammert and Benjamin Parrell},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3201--3205},
doi = {10.... | When speakers are exposed to auditory feedback perturbations of a particular
vowel, they not only adapt their productions of that vowel but also
transfer this change to other, untrained, vowels. However, current
models of speech sensorimotor adaptation, which rely on changes in
the feedforward control of specific speec... | null | null |
kawahara21_interspeech | Mixture of Orthogonal Sequences Made from Extended Time-Stretched Pulses Enables Measurement of Involuntary Voice Fundamental Frequency Response to Pitch Perturbation | [
"Hideki Kawahara",
"Toshie Matsui",
"Kohei Yatabe",
"Ken-Ichi Sakakibara",
"Minoru Tsuzaki",
"Masanori Morise",
"Toshio Irino"
] | https://www.isca-archive.org/interspeech_2021/kawahara21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kawahara21_interspeech.pdf | 10.21437/Interspeech.2021-2073 | 3206-3210 | @inproceedings{kawahara21_interspeech,
title = {{Mixture of Orthogonal Sequences Made from Extended Time-Stretched Pulses Enables Measurement of Involuntary Voice Fundamental Frequency Response to Pitch Perturbation}},
author = {Hideki Kawahara and Toshie Matsui and Kohei Yatabe and Ken-Ichi Sakakibara and M... | Auditory feedback plays an essential role in the regulation of the
fundamental frequency of voiced sounds. The fundamental frequency also
responds to auditory stimulation other than the speaker’s voice.
We propose to use this response of the fundamental frequency of sustained
vowels to frequency-modulated test signals ... | 2104.01444 | title_snapshot |
you21c_interspeech | Contextualized Attention-Based Knowledge Transfer for Spoken Conversational Question Answering | [
"Chenyu You",
"Nuo Chen",
"Yuexian Zou"
] | https://www.isca-archive.org/interspeech_2021/you21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/you21c_interspeech.pdf | 10.21437/Interspeech.2021-110 | 3211-3215 | @inproceedings{you21c_interspeech,
title = {{Contextualized Attention-Based Knowledge Transfer for Spoken Conversational Question Answering}},
author = {Chenyu You and Nuo Chen and Yuexian Zou},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3211--3215},
doi = {10.21437/Inte... | Spoken conversational question answering (SCQA) requires machines to
model the flow of multi-turn conversation given the speech utterances
and text corpora. Different from traditional text question answering
(QA) tasks, SCQA involves audio signal processing, passage comprehension,
and contextual understanding. However,... | 2010.11066 | title_snapshot |
duan21_interspeech | Injecting Descriptive Meta-Information into Pre-Trained Language Models with Hypernetworks | [
"Wenying Duan",
"Xiaoxi He",
"Zimu Zhou",
"Hong Rao",
"Lothar Thiele"
] | https://www.isca-archive.org/interspeech_2021/duan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/duan21_interspeech.pdf | 10.21437/Interspeech.2021-229 | 3216-3220 | @inproceedings{duan21_interspeech,
title = {{Injecting Descriptive Meta-Information into Pre-Trained Language Models with Hypernetworks}},
author = {Wenying Duan and Xiaoxi He and Zimu Zhou and Hong Rao and Lothar Thiele},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3216--3220},
... | Pre-trained language models have been widely adopted as backbones in
various natural language processing tasks. However, existing pre-trained
language models ignore the descriptive meta-information in the text
such as the distinction between the title and the mainbody, leading
to over-weighted attention to insignifican... | null | null |
rohmatillah21_interspeech | Causal Confusion Reduction for Robust Multi-Domain Dialogue Policy | [
"Mahdin Rohmatillah",
"Jen-Tzung Chien"
] | https://www.isca-archive.org/interspeech_2021/rohmatillah21_interspeech.html | https://www.isca-archive.org/interspeech_2021/rohmatillah21_interspeech.pdf | 10.21437/Interspeech.2021-534 | 3221-3225 | @inproceedings{rohmatillah21_interspeech,
title = {{Causal Confusion Reduction for Robust Multi-Domain Dialogue Policy}},
author = {Mahdin Rohmatillah and Jen-Tzung Chien},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3221--3225},
doi = {10.21437/Interspeech.2021-534},
i... | In the multi-domain dialogue system, dialog policy plays an important
role since it determines the suitable actions based on the user’s
goals. However, in many recent works, most of the dialogue optimizations,
especially that use reinforcement learning (RL) methods, do not perform
well. The main problem is that the ini... | null | null |
fujie21_interspeech | Timing Generating Networks: Neural Network Based Precise Turn-Taking Timing Prediction in Multiparty Conversation | [
"Shinya Fujie",
"Hayato Katayama",
"Jin Sakuma",
"Tetsunori Kobayashi"
] | https://www.isca-archive.org/interspeech_2021/fujie21_interspeech.html | https://www.isca-archive.org/interspeech_2021/fujie21_interspeech.pdf | 10.21437/Interspeech.2021-874 | 3226-3230 | @inproceedings{fujie21_interspeech,
title = {{Timing Generating Networks: Neural Network Based Precise Turn-Taking Timing Prediction in Multiparty Conversation}},
author = {Shinya Fujie and Hayato Katayama and Jin Sakuma and Tetsunori Kobayashi},
year = {2021},
booktitle = {{Interspeech 2021}},
pa... | A brand new neural network based precise timing generation framework,
named the Timing Generating Network (TGN), is proposed and applied
to turn-taking timing decision problems. Although turn-taking problems
have conventionally been formalized as users’ end-of-turn detection,
this approach cannot estimate the precise t... | null | null |
chen21n_interspeech | Human-to-Human Conversation Dataset for Learning Fine-Grained Turn-Taking Action | [
"Kehan Chen",
"Zezhong Li",
"Suyang Dai",
"Wei Zhou",
"Haiqing Chen"
] | https://www.isca-archive.org/interspeech_2021/chen21n_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21n_interspeech.pdf | 10.21437/Interspeech.2021-994 | 3231-3235 | @inproceedings{chen21n_interspeech,
title = {{Human-to-Human Conversation Dataset for Learning Fine-Grained Turn-Taking Action}},
author = {Kehan Chen and Zezhong Li and Suyang Dai and Wei Zhou and Haiqing Chen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3231--3235},
doi ... | Conducting natural turn-taking behavior takes a crucial part in the
user experience of modern spoken dialogue systems. One way to build
such system is to learn those behaviors from real-world human-to-human
dialogues, which have the most diverse and fine-grained turn-taking
actions than any manual constructed sessions.... | null | null |
sundararaman21_interspeech | PhonemeBERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript | [
"Mukuntha Narayanan Sundararaman",
"Ayush Kumar",
"Jithendra Vepa"
] | https://www.isca-archive.org/interspeech_2021/sundararaman21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sundararaman21_interspeech.pdf | 10.21437/Interspeech.2021-1582 | 3236-3240 | @inproceedings{sundararaman21_interspeech,
title = {{PhonemeBERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript}},
author = {Mukuntha Narayanan Sundararaman and Ayush Kumar and Jithendra Vepa},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3236--3240},
doi ... | Recent years have witnessed significant improvement in ASR systems
to recognize spoken utterances. However, it is still a challenging
task for noisy and out-of-domain data, where ASR errors are prevalent
in the transcribed text. These errors significantly degrade the performance
of downstream tasks such as intent and s... | 2102.00804 | title_judge |
luo21d_interspeech | Joint Retrieval-Extraction Training for Evidence-Aware Dialog Response Selection | [
"Hongyin Luo",
"James Glass",
"Garima Lalwani",
"Yi Zhang",
"Shang-Wen Li"
] | https://www.isca-archive.org/interspeech_2021/luo21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/luo21d_interspeech.pdf | 10.21437/Interspeech.2021-1689 | 3241-3245 | @inproceedings{luo21d_interspeech,
title = {{Joint Retrieval-Extraction Training for Evidence-Aware Dialog Response Selection}},
author = {Hongyin Luo and James Glass and Garima Lalwani and Yi Zhang and Shang-Wen Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3241--3245},
doi... | Neural dialog response selection models infer by scoring each candidate
response given the dialog context, and the cross-encoder method yields
state-of-the-art (SOTA) results for the task. In the method, the candidate
scores are computed by feeding the output embedding of the first token
in the input sequence, which is... | null | null |
shenoy21_interspeech | Adapting Long Context NLM for ASR Rescoring in Conversational Agents | [
"Ashish Shenoy",
"Sravan Bodapati",
"Monica Sunkara",
"Srikanth Ronanki",
"Katrin Kirchhoff"
] | https://www.isca-archive.org/interspeech_2021/shenoy21_interspeech.html | https://www.isca-archive.org/interspeech_2021/shenoy21_interspeech.pdf | 10.21437/Interspeech.2021-1849 | 3246-3250 | @inproceedings{shenoy21_interspeech,
title = {{Adapting Long Context NLM for ASR Rescoring in Conversational Agents}},
author = {Ashish Shenoy and Sravan Bodapati and Monica Sunkara and Srikanth Ronanki and Katrin Kirchhoff},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3246--3250... | Neural Language Models (NLM), when trained and evaluated with context
spanning multiple utterances, have been shown to consistently outperform
both conventional n-gram language models and NLMs that use limited
context. In this paper, we investigate various techniques to incorporate
turn based context history into both ... | 2104.11070 | title_snapshot |
li21h_interspeech | Oriental Language Recognition (OLR) 2020: Summary and Analysis | [
"Jing Li",
"Binling Wang",
"Yiming Zhi",
"Zheng Li",
"Lin Li",
"Qingyang Hong",
"Dong Wang"
] | https://www.isca-archive.org/interspeech_2021/li21h_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21h_interspeech.pdf | 10.21437/Interspeech.2021-2171 | 3251-3255 | @inproceedings{li21h_interspeech,
title = {{Oriental Language Recognition (OLR) 2020: Summary and Analysis}},
author = {Jing Li and Binling Wang and Yiming Zhi and Zheng Li and Lin Li and Qingyang Hong and Dong Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3251--3255},
doi... | The fifth Oriental Language Recognition (OLR) Challenge focuses on
language recognition in a variety of complex environments to promote
its development. The OLR 2020 Challenge includes three tasks: (1) cross-channel
language identification, (2) dialect identification, and (3) noisy
language identification. We choose C ... | 2107.05365 | title_snapshot |
duroselle21b_interspeech | Language Recognition on Unknown Conditions: The LORIA-Inria-MULTISPEECH System for AP20-OLR Challenge | [
"Raphaël Duroselle",
"Md. Sahidullah",
"Denis Jouvet",
"Irina Illina"
] | https://www.isca-archive.org/interspeech_2021/duroselle21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/duroselle21b_interspeech.pdf | 10.21437/Interspeech.2021-276 | 3256-3260 | @inproceedings{duroselle21b_interspeech,
title = {{Language Recognition on Unknown Conditions: The LORIA-Inria-MULTISPEECH System for AP20-OLR Challenge}},
author = {Raphaël Duroselle and Md. Sahidullah and Denis Jouvet and Irina Illina},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | We describe the LORIA-Inria-MULTISPEECH system submitted to the Oriental
Language Recognition AP20-OLR Challenge. This system has been specifically
designed to be robust to unknown conditions: channel mismatch (task
1) and noisy conditions (task 3). Three sets of studies have been carried
out for elaborating the system... | null | null |
kong21b_interspeech | Dynamic Multi-Scale Convolution for Dialect Identification | [
"Tianlong Kong",
"Shouyi Yin",
"Dawei Zhang",
"Wang Geng",
"Xin Wang",
"Dandan Song",
"Jinwen Huang",
"Huiyu Shi",
"Xiaorui Wang"
] | https://www.isca-archive.org/interspeech_2021/kong21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/kong21b_interspeech.pdf | 10.21437/Interspeech.2021-56 | 3261-3265 | @inproceedings{kong21b_interspeech,
title = {{Dynamic Multi-Scale Convolution for Dialect Identification}},
author = {Tianlong Kong and Shouyi Yin and Dawei Zhang and Wang Geng and Xin Wang and Dandan Song and Jinwen Huang and Huiyu Shi and Xiaorui Wang},
year = {2021},
booktitle = {{Interspeech 202... | Time Delay Neural Networks (TDNN)-based methods are widely used in
dialect identification. However, in previous work with TDNN application,
subtle variant is being neglected in different feature scales. To address
this issue, we propose a new architecture, named dynamic multi-scale
convolution, which consists of dynami... | 2108.07787 | title_snapshot |
wang21z_interspeech | An End-to-End Dialect Identification System with Transfer Learning from a Multilingual Automatic Speech Recognition Model | [
"Ding Wang",
"Shuaishuai Ye",
"Xinhui Hu",
"Sheng Li",
"Xinkang Xu"
] | https://www.isca-archive.org/interspeech_2021/wang21z_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21z_interspeech.pdf | 10.21437/Interspeech.2021-374 | 3266-3270 | @inproceedings{wang21z_interspeech,
title = {{An End-to-End Dialect Identification System with Transfer Learning from a Multilingual Automatic Speech Recognition Model}},
author = {Ding Wang and Shuaishuai Ye and Xinhui Hu and Sheng Li and Xinkang Xu},
year = {2021},
booktitle = {{Interspeech 2021}}... | In this paper, we propose an end-to-end (E2E) dialect identification
system trained using transfer learning from a multilingual automatic
speech recognition (ASR) model. This is also an extension of our submitted
system to the Oriental Language Recognition Challenge 2020 (AP20-OLR).
We verified its applicability using ... | null | null |
yu21b_interspeech | Language Recognition Based on Unsupervised Pretrained Models | [
"Haibin Yu",
"Jing Zhao",
"Song Yang",
"Zhongqin Wu",
"Yuting Nie",
"Wei-Qiang Zhang"
] | https://www.isca-archive.org/interspeech_2021/yu21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/yu21b_interspeech.pdf | 10.21437/Interspeech.2021-807 | 3271-3275 | @inproceedings{yu21b_interspeech,
title = {{Language Recognition Based on Unsupervised Pretrained Models}},
author = {Haibin Yu and Jing Zhao and Song Yang and Zhongqin Wu and Yuting Nie and Wei-Qiang Zhang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3271--3275},
doi = ... | Unsupervised pretrained models have been proven to rival or even outperform
supervised systems in various speech recognition tasks. However, their
performance for language recognition is still left to be explored.
In this paper, we construct several language recognition systems based
on existing unsupervised pretrainin... | null | null |
li21i_interspeech | Additive Phoneme-Aware Margin Softmax Loss for Language Recognition | [
"Zheng Li",
"Yan Liu",
"Lin Li",
"Qingyang Hong"
] | https://www.isca-archive.org/interspeech_2021/li21i_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21i_interspeech.pdf | 10.21437/Interspeech.2021-1167 | 3276-3280 | @inproceedings{li21i_interspeech,
title = {{Additive Phoneme-Aware Margin Softmax Loss for Language Recognition}},
author = {Zheng Li and Yan Liu and Lin Li and Qingyang Hong},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3276--3280},
doi = {10.21437/Interspeech.2021-1167}... | This paper proposes an additive phoneme-aware margin softmax (APM-Softmax)
loss to train the multi-task learning network with phonetic information
for language recognition. In additive margin softmax (AM-Softmax) loss,
the margin is set as a constant during the entire training for all
training samples, and that is a su... | 2106.12851 | title_snapshot |
jahchan21_interspeech | Towards an Accent-Robust Approach for ATC Communications Transcription | [
"Nataly Jahchan",
"Florentin Barbier",
"Ariyanidevi Dharma Gita",
"Khaled Khelif",
"Estelle Delpech"
] | https://www.isca-archive.org/interspeech_2021/jahchan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/jahchan21_interspeech.pdf | 10.21437/Interspeech.2021-333 | 3281-3285 | @inproceedings{jahchan21_interspeech,
title = {{Towards an Accent-Robust Approach for ATC Communications Transcription}},
author = {Nataly Jahchan and Florentin Barbier and Ariyanidevi Dharma Gita and Khaled Khelif and Estelle Delpech},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = ... | Air Traffic Control (ATC) communications are a typical example where
Automatic Speech Recognition could face various challenges: audio data
are quite noisy due to the characteristics of capturing mechanisms.
All speakers involved use a specific English-based phraseology and
a significant number of pilots and controller... | null | null |
szoke21_interspeech | Detecting English Speech in the Air Traffic Control Voice Communication | [
"Igor Szöke",
"Santosh Kesiraju",
"Ondřej Novotný",
"Martin Kocour",
"Karel Veselý",
"Jan Černocký"
] | https://www.isca-archive.org/interspeech_2021/szoke21_interspeech.html | https://www.isca-archive.org/interspeech_2021/szoke21_interspeech.pdf | 10.21437/Interspeech.2021-1033 | 3286-3290 | @inproceedings{szoke21_interspeech,
title = {{Detecting English Speech in the Air Traffic Control Voice Communication}},
author = {Igor Szöke and Santosh Kesiraju and Ondřej Novotný and Martin Kocour and Karel Veselý and Jan Černocký},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {... | Developing in-cockpit voice enabled applications require a real-world
dataset with labels and annotations. We launched a community platform
for collecting the Air-Traffic Control (ATC) speech, world-wide in
the ATCO 2 project. Filtering out non-English speech is one
of the main components in the data processing pipelin... | 2104.02332 | title_snapshot |
ohneiser21_interspeech | Robust Command Recognition for Lithuanian Air Traffic Control Tower Utterances | [
"Oliver Ohneiser",
"Seyyed Saeed Sarfjoo",
"Hartmut Helmke",
"Shruthi Shetty",
"Petr Motlicek",
"Matthias Kleinert",
"Heiko Ehr",
"Šarūnas Murauskas"
] | https://www.isca-archive.org/interspeech_2021/ohneiser21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ohneiser21_interspeech.pdf | 10.21437/Interspeech.2021-935 | 3291-3295 | @inproceedings{ohneiser21_interspeech,
title = {{Robust Command Recognition for Lithuanian Air Traffic Control Tower Utterances}},
author = {Oliver Ohneiser and Seyyed Saeed Sarfjoo and Hartmut Helmke and Shruthi Shetty and Petr Motlicek and Matthias Kleinert and Heiko Ehr and Šarūnas Murauskas},
year ... | The maturity of automatic speech recognition (ASR) systems at controller
working positions is currently a highly relevant technological topic
in air traffic control (ATC). However, ATC service providers are less
interested in pure word error rate (WER). They want to see benefits
of ASR applications for ATC. Such applic... | null | null |
zuluagagomez21_interspeech | Contextual Semi-Supervised Learning: An Approach to Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems | [
"Juan Zuluaga-Gomez",
"Iuliia Nigmatulina",
"Amrutha Prasad",
"Petr Motlicek",
"Karel Veselý",
"Martin Kocour",
"Igor Szöke"
] | https://www.isca-archive.org/interspeech_2021/zuluagagomez21_interspeech.html | https://www.isca-archive.org/interspeech_2021/zuluagagomez21_interspeech.pdf | 10.21437/Interspeech.2021-1373 | 3296-3300 | @inproceedings{zuluagagomez21_interspeech,
title = {{Contextual Semi-Supervised Learning: An Approach to Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems}},
author = {Juan Zuluaga-Gomez and Iuliia Nigmatulina and Amrutha Prasad and Petr Motlicek and Karel Veselý and Martin Kocour and Igor ... | Air traffic management and specifically air-traffic control (ATC) rely
mostly on voice communications between Air Traffic Controllers (ATCos)
and pilots. In most cases, these voice communications follow a well-defined
grammar that could be leveraged in Automatic Speech Recognition (ASR)
technologies. The callsign used ... | 2104.03643 | title_snapshot |
kocour21_interspeech | Boosting of Contextual Information in ASR for Air-Traffic Call-Sign Recognition | [
"Martin Kocour",
"Karel Veselý",
"Alexander Blatt",
"Juan Zuluaga Gomez",
"Igor Szöke",
"Jan Černocký",
"Dietrich Klakow",
"Petr Motlicek"
] | https://www.isca-archive.org/interspeech_2021/kocour21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kocour21_interspeech.pdf | 10.21437/Interspeech.2021-1619 | 3301-3305 | @inproceedings{kocour21_interspeech,
title = {{Boosting of Contextual Information in ASR for Air-Traffic Call-Sign Recognition}},
author = {Martin Kocour and Karel Veselý and Alexander Blatt and Juan Zuluaga Gomez and Igor Szöke and Jan Černocký and Dietrich Klakow and Petr Motlicek},
year = {2021},
... | Contextual adaptation of ASR can be very beneficial for multi-accent
and often noisy Air-Traffic Control (ATC) speech. Our focus is call-sign
recognition, which can be used to track conversations of ATC operators
with individual airplanes. We developed a two-stage boosting strategy,
consisting of HCLG boosting and Latt... | null | null |
elie21_interspeech | Modeling the Effect of Military Oxygen Masks on Speech Characteristics | [
"Benjamin Elie",
"Jodie Gauvain",
"Jean-Luc Gauvain",
"Lori Lamel"
] | https://www.isca-archive.org/interspeech_2021/elie21_interspeech.html | https://www.isca-archive.org/interspeech_2021/elie21_interspeech.pdf | 10.21437/Interspeech.2021-1650 | 3306-3310 | @inproceedings{elie21_interspeech,
title = {{Modeling the Effect of Military Oxygen Masks on Speech Characteristics}},
author = {Benjamin Elie and Jodie Gauvain and Jean-Luc Gauvain and Lori Lamel},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3306--3310},
doi = {10.21437/... | Wearing an oxygen mask changes the speech production of speakers. It
indeed modifies the vocal apparatus and perturbs the articulatory movements
of the speaker. This paper studies the impact of the oxygen mask of
military aircraft pilots on formant trajectories, both dynamically
(variations of the formants at a utteran... | null | null |
ribeiro21b_interspeech | Towards the Prediction of the Vocal Tract Shape from the Sequence of Phonemes to be Articulated | [
"Vinicius Ribeiro",
"Karyna Isaieva",
"Justine Leclere",
"Pierre-André Vuissoz",
"Yves Laprie"
] | https://www.isca-archive.org/interspeech_2021/ribeiro21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/ribeiro21b_interspeech.pdf | 10.21437/Interspeech.2021-184 | 3325-3329 | @inproceedings{ribeiro21b_interspeech,
title = {{Towards the Prediction of the Vocal Tract Shape from the Sequence of Phonemes to be Articulated}},
author = {Vinicius Ribeiro and Karyna Isaieva and Justine Leclere and Pierre-André Vuissoz and Yves Laprie},
year = {2021},
booktitle = {{Interspeech 20... | In this work, we address the prediction of speech articulators’
temporal geometric position from the sequence of phonemes to be articulated.
We start from a set of real-time MRI sequences uttered by a female
French speaker. The contours of five articulators were tracked automatically
in each of the frames in the MRI vi... | null | null |
blandin21_interspeech | Comparison of the Finite Element Method, the Multimodal Method and the Transmission-Line Model for the Computation of Vocal Tract Transfer Functions | [
"Rémi Blandin",
"Marc Arnela",
"Simon Félix",
"Jean-Baptiste Doc",
"Peter Birkholz"
] | https://www.isca-archive.org/interspeech_2021/blandin21_interspeech.html | https://www.isca-archive.org/interspeech_2021/blandin21_interspeech.pdf | 10.21437/Interspeech.2021-975 | 3330-3334 | @inproceedings{blandin21_interspeech,
title = {{Comparison of the Finite Element Method, the Multimodal Method and the Transmission-Line Model for the Computation of Vocal Tract Transfer Functions}},
author = {Rémi Blandin and Marc Arnela and Simon Félix and Jean-Baptiste Doc and Peter Birkholz},
year ... | The acoustic properties of vocal tract are usually characterized by
its transfer function from the input acoustic volume flow at the glottis
to the radiated acoustic pressure. These transfer functions can be
computed with acoustic models. Three-dimensional acoustic simulation
are used to take into account accurately th... | null | null |
wagner21b_interspeech | Effects of Time Pressure and Spontaneity on Phonotactic Innovations in German Dialogues | [
"Petra Wagner",
"Sina Zarrieß",
"Joana Cholin"
] | https://www.isca-archive.org/interspeech_2021/wagner21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/wagner21b_interspeech.pdf | 10.21437/Interspeech.2021-1539 | 3335-3339 | @inproceedings{wagner21b_interspeech,
title = {{Effects of Time Pressure and Spontaneity on Phonotactic Innovations in German Dialogues}},
author = {Petra Wagner and Sina Zarrieß and Joana Cholin},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3335--3339},
doi = {10.21437/I... | Speech variation is often explained by speakers’ balancing of
production constraints (favoring phonetic reduction of high frequency,
expected items) and listener orientation (favoring more canonical productions
for low frequency, unexpected items). Less well understood are processes
involving a structural reorganizatio... | null | null |
medina21_interspeech | Importance of Parasagittal Sensor Information in Tongue Motion Capture Through a Diphonic Analysis | [
"Salvador Medina",
"Sarah Taylor",
"Mark Tiede",
"Alexander Hauptmann",
"Iain Matthews"
] | https://www.isca-archive.org/interspeech_2021/medina21_interspeech.html | https://www.isca-archive.org/interspeech_2021/medina21_interspeech.pdf | 10.21437/Interspeech.2021-1732 | 3340-3344 | @inproceedings{medina21_interspeech,
title = {{Importance of Parasagittal Sensor Information in Tongue Motion Capture Through a Diphonic Analysis}},
author = {Salvador Medina and Sarah Taylor and Mark Tiede and Alexander Hauptmann and Iain Matthews},
year = {2021},
booktitle = {{Interspeech 2021}},
... | Our study examines the information obtained by adding two parasagittal
sensors to the standard midsagittal configuration of an Electromagnetic
Articulography (EMA) observation of lingual articulation. In this work,
we present a large and phonetically balanced corpus obtained from an
EMA recording session of a single En... | null | null |
georges21_interspeech | Learning Robust Speech Representation with an Articulatory-Regularized Variational Autoencoder | [
"Marc-Antoine Georges",
"Laurent Girin",
"Jean-Luc Schwartz",
"Thomas Hueber"
] | https://www.isca-archive.org/interspeech_2021/georges21_interspeech.html | https://www.isca-archive.org/interspeech_2021/georges21_interspeech.pdf | 10.21437/Interspeech.2021-1604 | 3345-3349 | @inproceedings{georges21_interspeech,
title = {{Learning Robust Speech Representation with an Articulatory-Regularized Variational Autoencoder}},
author = {Marc-Antoine Georges and Laurent Girin and Jean-Luc Schwartz and Thomas Hueber},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = ... | It is increasingly considered that human speech perception and production
both rely on articulatory representations. In this paper, we investigate
whether this type of representation could improve the performances
of a deep generative model (here a variational autoencoder) trained
to encode and decode acoustic speech f... | 2104.03204 | title_snapshot |
weston21_interspeech | Changes in Glottal Source Parameter Values with Light to Moderate Physical Load | [
"Heather Weston",
"Laura L. Koenig",
"Susanne Fuchs"
] | https://www.isca-archive.org/interspeech_2021/weston21_interspeech.html | https://www.isca-archive.org/interspeech_2021/weston21_interspeech.pdf | 10.21437/Interspeech.2021-1881 | 3350-3354 | @inproceedings{weston21_interspeech,
title = {{Changes in Glottal Source Parameter Values with Light to Moderate Physical Load}},
author = {Heather Weston and Laura L. Koenig and Susanne Fuchs},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3350--3354},
doi = {10.21437/Inte... | Engaging in everyday physical activities, like walking, initiates physiological
processes that also affect parts of the body used for speech. However,
it is currently unclear to what extent such activities affect phonatory
processes, and in turn, the voice. The present exploratory study investigates
how selected glotta... | null | null |
vali21_interspeech | End-to-End Optimized Multi-Stage Vector Quantization of Spectral Envelopes for Speech and Audio Coding | [
"Mohammad Hassan Vali",
"Tom Bäckström"
] | https://www.isca-archive.org/interspeech_2021/vali21_interspeech.html | https://www.isca-archive.org/interspeech_2021/vali21_interspeech.pdf | 10.21437/Interspeech.2021-867 | 3355-3359 | @inproceedings{vali21_interspeech,
title = {{End-to-End Optimized Multi-Stage Vector Quantization of Spectral Envelopes for Speech and Audio Coding}},
author = {Mohammad Hassan Vali and Tom Bäckström},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3355--3359},
doi = {10.214... | Spectral envelope modeling is an instrumental part of speech and audio
codecs, which can be used to enable efficient entropy coding of spectral
components. Overall optimization of codecs, including envelope models,
has however been difficult due to the complicated interactions between
different modules of the codec. In... | null | null |
nareddula21_interspeech | Fusion-Net: Time-Frequency Information Fusion Y-Network for Speech Enhancement | [
"Santhan Kumar Reddy Nareddula",
"Subrahmanyam Gorthi",
"Rama Krishna Sai S. Gorthi"
] | https://www.isca-archive.org/interspeech_2021/nareddula21_interspeech.html | https://www.isca-archive.org/interspeech_2021/nareddula21_interspeech.pdf | 10.21437/Interspeech.2021-1184 | 3360-3364 | @inproceedings{nareddula21_interspeech,
title = {{Fusion-Net: Time-Frequency Information Fusion Y-Network for Speech Enhancement}},
author = {Santhan Kumar Reddy Nareddula and Subrahmanyam Gorthi and Rama Krishna Sai S. Gorthi},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3360--3... | This paper proposes a deep learning-based densely connected Y-Net as
an effective network architecture for the fusion of time and frequency
domain loss functions for speech enhancement. The proposed architecture
performs speech enhancement in the time domain while fusing information
from the frequency domain. Y-network... | null | null |
marcinek21_interspeech | N-MTTL SI Model: Non-Intrusive Multi-Task Transfer Learning-Based Speech Intelligibility Prediction Model with Scenery Classification | [
"Ľuboš Marcinek",
"Michael Stone",
"Rebecca Millman",
"Patrick Gaydecki"
] | https://www.isca-archive.org/interspeech_2021/marcinek21_interspeech.html | https://www.isca-archive.org/interspeech_2021/marcinek21_interspeech.pdf | 10.21437/Interspeech.2021-1878 | 3365-3369 | @inproceedings{marcinek21_interspeech,
title = {{N-MTTL SI Model: Non-Intrusive Multi-Task Transfer Learning-Based Speech Intelligibility Prediction Model with Scenery Classification}},
author = {Ľuboš Marcinek and Michael Stone and Rebecca Millman and Patrick Gaydecki},
year = {2021},
booktitle = {... | The application of speech enhancement algorithms for hearing aids may
not always be beneficial to increasing speech intelligibility. Therefore,
a prior environment classification could be important. However, previous
speech intelligibility models do not provide any additional information
regarding the reason for a decr... | null | null |
xia21b_interspeech | Temporal Context in Speech Emotion Recognition | [
"Yangyang Xia",
"Li-Wei Chen",
"Alexander Rudnicky",
"Richard M. Stern"
] | https://www.isca-archive.org/interspeech_2021/xia21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/xia21b_interspeech.pdf | 10.21437/Interspeech.2021-1840 | 3370-3374 | @inproceedings{xia21b_interspeech,
title = {{Temporal Context in Speech Emotion Recognition}},
author = {Yangyang Xia and Li-Wei Chen and Alexander Rudnicky and Richard M. Stern},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3370--3374},
doi = {10.21437/Interspeech.2021-18... | We investigate the importance of temporal context for speech emotion
recognition (SER). Two SER systems trained on traditional and learned
features, respectively, are developed to predict categorical labels
of emotion. For traditional acoustical features, we study the combination
of filterbank features and prosodic fea... | null | null |
li21j_interspeech | Learning Fine-Grained Cross Modality Excitement for Speech Emotion Recognition | [
"Hang Li",
"Wenbiao Ding",
"Zhongqin Wu",
"Zitao Liu"
] | https://www.isca-archive.org/interspeech_2021/li21j_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21j_interspeech.pdf | 10.21437/Interspeech.2021-158 | 3375-3379 | @inproceedings{li21j_interspeech,
title = {{Learning Fine-Grained Cross Modality Excitement for Speech Emotion Recognition}},
author = {Hang Li and Wenbiao Ding and Zhongqin Wu and Zitao Liu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3375--3379},
doi = {10.21437/Inters... | Speech emotion recognition is a challenging task because the emotion
expression is complex, multimodal and fine-grained. In this paper,
we propose a novel multimodal deep learning approach to perform fine-grained
emotion recognition from real-life speeches. We design a temporal alignment
mean-max pooling mechanism to c... | 2010.12733 | title_snapshot |
vaaras21_interspeech | Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit | [
"Einari Vaaras",
"Sari Ahlqvist-Björkroth",
"Konstantinos Drossos",
"Okko Räsänen"
] | https://www.isca-archive.org/interspeech_2021/vaaras21_interspeech.html | https://www.isca-archive.org/interspeech_2021/vaaras21_interspeech.pdf | 10.21437/Interspeech.2021-303 | 3380-3384 | @inproceedings{vaaras21_interspeech,
title = {{Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit}},
author = {Einari Vaaras and Sari Ahlqvist-Björkroth and Konstantinos Drossos and Okko Räsänen},
year = {2021},
booktitle = ... | Researchers have recently started to study how the emotional speech
heard by young infants can affect their developmental outcomes. As
a part of this research, hundreds of hours of daylong recordings from
preterm infants’ audio environments were collected from two hospitals
in Finland and Estonia in the context of so-c... | 2106.09539 | title_snapshot |
qian21_interspeech | Multimodal Sentiment Analysis with Temporal Modality Attention | [
"Fan Qian",
"Jiqing Han"
] | https://www.isca-archive.org/interspeech_2021/qian21_interspeech.html | https://www.isca-archive.org/interspeech_2021/qian21_interspeech.pdf | 10.21437/Interspeech.2021-487 | 3385-3389 | @inproceedings{qian21_interspeech,
title = {{Multimodal Sentiment Analysis with Temporal Modality Attention}},
author = {Fan Qian and Jiqing Han},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3385--3389},
doi = {10.21437/Interspeech.2021-487},
issn = {2958-1796},
} | Multimodal sentiment analysis is an important research that involves
integrating information from multiple modalities to identify a speaker
underlying attitude. The core challenge is to model cross-modal interactions
which span across both the different modalities and time. Although
great progress has been made, the ex... | null | null |
t21_interspeech | Stochastic Process Regression for Cross-Cultural Speech Emotion Recognition | [
"Mani Kumar T",
"Enrique Sanchez",
"Georgios Tzimiropoulos",
"Timo Giesbrecht",
"Michel Valstar"
] | https://www.isca-archive.org/interspeech_2021/t21_interspeech.html | https://www.isca-archive.org/interspeech_2021/t21_interspeech.pdf | 10.21437/Interspeech.2021-610 | 3390-3394 | @inproceedings{t21_interspeech,
title = {{Stochastic Process Regression for Cross-Cultural Speech Emotion Recognition}},
author = {Mani Kumar T and Enrique Sanchez and Georgios Tzimiropoulos and Timo Giesbrecht and Michel Valstar},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3390... | In this work, we pose continuous apparent emotion recognition from
speech as a problem of learning distributions of functions, and do
so using Stochastic Processes Regression. We presume that the relation
between speech signals and their corresponding emotion labels is governed
by some underlying stochastic process, in... | null | null |
li21k_interspeech | Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition | [
"Haoqi Li",
"Yelin Kim",
"Cheng-Hao Kuo",
"Shrikanth S. Narayanan"
] | https://www.isca-archive.org/interspeech_2021/li21k_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21k_interspeech.pdf | 10.21437/Interspeech.2021-666 | 3395-3399 | @inproceedings{li21k_interspeech,
title = {{Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition}},
author = {Haoqi Li and Yelin Kim and Cheng-Hao Kuo and Shrikanth S. Narayanan},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3395--... | Key challenges in developing generalized automatic emotion recognition
systems include scarcity of labeled data and lack of gold-standard
references. Even for the cues that are labeled as the same emotion
category, the variability of associated expressions can be high depending
on the elicitation context e.g., emotion ... | 2104.01978 | title_snapshot |
pepino21_interspeech | Emotion Recognition from Speech Using wav2vec 2.0 Embeddings | [
"Leonardo Pepino",
"Pablo Riera",
"Luciana Ferrer"
] | https://www.isca-archive.org/interspeech_2021/pepino21_interspeech.html | https://www.isca-archive.org/interspeech_2021/pepino21_interspeech.pdf | 10.21437/Interspeech.2021-703 | 3400-3404 | @inproceedings{pepino21_interspeech,
title = {{Emotion Recognition from Speech Using wav2vec 2.0 Embeddings}},
author = {Leonardo Pepino and Pablo Riera and Luciana Ferrer},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3400--3404},
doi = {10.21437/Interspeech.2021-703},
... | Emotion recognition datasets are relatively small, making the use of
deep learning techniques challenging. In this work, we propose a transfer
learning method for speech emotion recognition (SER) where features
extracted from pre-trained wav2vec 2.0 models are used as input to
shallow neural networks to recognize emoti... | 2104.03502 | title_snapshot |
liu21k_interspeech | Graph Isomorphism Network for Speech Emotion Recognition | [
"Jiawang Liu",
"Haoxiang Wang"
] | https://www.isca-archive.org/interspeech_2021/liu21k_interspeech.html | https://www.isca-archive.org/interspeech_2021/liu21k_interspeech.pdf | 10.21437/Interspeech.2021-1154 | 3405-3409 | @inproceedings{liu21k_interspeech,
title = {{Graph Isomorphism Network for Speech Emotion Recognition}},
author = {Jiawang Liu and Haoxiang Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3405--3409},
doi = {10.21437/Interspeech.2021-1154},
issn = {2958-1796},
} | Previous deep learning approaches such as Convolutional Neural Network
(CNN) and Long Short-Term Memory (LSTM) have been broadly used in speech
emotion recognition (SER). In these approaches, speech signals are
generally modeled in the Euclidean space. In this paper, a novel SER
model (LSTM-GIN) is proposed, which appl... | null | null |
kumawat21_interspeech | Applying TDNN Architectures for Analyzing Duration Dependencies on Speech Emotion Recognition | [
"Pooja Kumawat",
"Aurobinda Routray"
] | https://www.isca-archive.org/interspeech_2021/kumawat21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kumawat21_interspeech.pdf | 10.21437/Interspeech.2021-2168 | 3410-3414 | @inproceedings{kumawat21_interspeech,
title = {{Applying TDNN Architectures for Analyzing Duration Dependencies on Speech Emotion Recognition}},
author = {Pooja Kumawat and Aurobinda Routray},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3410--3414},
doi = {10.21437/Inters... | We have analyzed the Time Delay Neural Network (TDNN) based architectures
for speech emotion classification. TDNN models efficiently capture
the temporal information and provide an utterance level prediction.
Emotions are dynamic in nature and require temporal context for reliable
prediction. In our work, we have appli... | null | null |
keesing21_interspeech | Acoustic Features and Neural Representations for Categorical Emotion Recognition from Speech | [
"Aaron Keesing",
"Yun Sing Koh",
"Michael Witbrock"
] | https://www.isca-archive.org/interspeech_2021/keesing21_interspeech.html | https://www.isca-archive.org/interspeech_2021/keesing21_interspeech.pdf | 10.21437/Interspeech.2021-2217 | 3415-3419 | @inproceedings{keesing21_interspeech,
title = {{Acoustic Features and Neural Representations for Categorical Emotion Recognition from Speech}},
author = {Aaron Keesing and Yun Sing Koh and Michael Witbrock},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3415--3419},
doi = {... | Many features have been proposed for use in speech emotion recognition,
from signal processing features to bag-of-audio-words (BoAW) models
to abstract neural representations. Some of these feature types have
not been directly compared across a large number of speech corpora
to determine performance differences. We pro... | null | null |
shon21_interspeech | Leveraging Pre-Trained Language Model for Speech Sentiment Analysis | [
"Suwon Shon",
"Pablo Brusco",
"Jing Pan",
"Kyu J. Han",
"Shinji Watanabe"
] | https://www.isca-archive.org/interspeech_2021/shon21_interspeech.html | https://www.isca-archive.org/interspeech_2021/shon21_interspeech.pdf | 10.21437/Interspeech.2021-1723 | 3420-3424 | @inproceedings{shon21_interspeech,
title = {{Leveraging Pre-Trained Language Model for Speech Sentiment Analysis}},
author = {Suwon Shon and Pablo Brusco and Jing Pan and Kyu J. Han and Shinji Watanabe},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3420--3424},
doi = {10.2... | In this paper, we explore the use of pre-trained language models to
learn sentiment information of written texts for speech sentiment analysis.
First, we investigate how useful a pre-trained language model would
be in a 2-step pipeline approach employing Automatic Speech Recognition
(ASR) and transcripts-based sentimen... | 2106.06598 | title_snapshot |
hou21b_interspeech | Cross-Domain Speech Recognition with Unsupervised Character-Level Distribution Matching | [
"Wenxin Hou",
"Jindong Wang",
"Xu Tan",
"Tao Qin",
"Takahiro Shinozaki"
] | https://www.isca-archive.org/interspeech_2021/hou21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/hou21b_interspeech.pdf | 10.21437/Interspeech.2021-57 | 3425-3429 | @inproceedings{hou21b_interspeech,
title = {{Cross-Domain Speech Recognition with Unsupervised Character-Level Distribution Matching}},
author = {Wenxin Hou and Jindong Wang and Xu Tan and Tao Qin and Takahiro Shinozaki},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3425--3429},
... | End-to-end automatic speech recognition (ASR) can achieve promising
performance with large-scale training data. However, it is known that
domain mismatch between training and testing data often leads to a
degradation of recognition accuracy. In this work, we focus on the
unsupervised domain adaptation for ASR and propo... | 2104.07491 | title_snapshot |
kanda21_interspeech | Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone | [
"Naoyuki Kanda",
"Guoli Ye",
"Yu Wu",
"Yashesh Gaur",
"Xiaofei Wang",
"Zhong Meng",
"Zhuo Chen",
"Takuya Yoshioka"
] | https://www.isca-archive.org/interspeech_2021/kanda21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kanda21_interspeech.pdf | 10.21437/Interspeech.2021-102 | 3430-3434 | @inproceedings{kanda21_interspeech,
title = {{Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone}},
author = {Naoyuki Kanda and Guoli Ye and Yu Wu and Yashesh Gaur and Xiaofei Wang and Zhong Meng and Zhuo Chen and Takuya Yoshioka},
year = {... | Transcribing meetings containing overlapped speech with only a single
distant microphone (SDM) has been one of the most challenging problems
for automatic speech recognition (ASR). While various approaches have
been proposed, all previous studies on the monaural overlapped speech
recognition problem were based on eithe... | 2103.16776 | title_snapshot |
lu21b_interspeech | On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer | [
"Liang Lu",
"Zhong Meng",
"Naoyuki Kanda",
"Jinyu Li",
"Yifan Gong"
] | https://www.isca-archive.org/interspeech_2021/lu21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/lu21b_interspeech.pdf | 10.21437/Interspeech.2021-161 | 3435-3439 | @inproceedings{lu21b_interspeech,
title = {{On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer}},
author = {Liang Lu and Zhong Meng and Naoyuki Kanda and Jinyu Li and Yifan Gong},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3435--3439},
doi = {10.... | Hybrid Autoregressive Transducer (HAT) is a recently proposed end-to-end
acoustic model that extends the standard Recurrent Neural Network Transducer
(RNN-T) for the purpose of the external language model (LM) fusion.
In HAT, the blank probability and the label probability are estimated
using two separate probability d... | 2010.12673 | title_snapshot |
kim21j_interspeech | Reducing Streaming ASR Model Delay with Self Alignment | [
"Jaeyoung Kim",
"Han Lu",
"Anshuman Tripathi",
"Qian Zhang",
"Hasim Sak"
] | https://www.isca-archive.org/interspeech_2021/kim21j_interspeech.html | https://www.isca-archive.org/interspeech_2021/kim21j_interspeech.pdf | 10.21437/Interspeech.2021-322 | 3440-3444 | @inproceedings{kim21j_interspeech,
title = {{Reducing Streaming ASR Model Delay with Self Alignment}},
author = {Jaeyoung Kim and Han Lu and Anshuman Tripathi and Qian Zhang and Hasim Sak},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3440--3444},
doi = {10.21437/Interspee... | Reducing prediction delay for streaming end-to-end ASR models with
minimal performance regression is a challenging problem. Constrained
alignment is a well-known existing approach that penalizes predicted
word boundaries using external low-latency acoustic models. On the
contrary, recently proposed FastEmit is a sequen... | 2105.05005 | title_snapshot |
diwan21b_interspeech | Reduce and Reconstruct: ASR for Low-Resource Phonetic Languages | [
"Anuj Diwan",
"Preethi Jyothi"
] | https://www.isca-archive.org/interspeech_2021/diwan21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/diwan21b_interspeech.pdf | 10.21437/Interspeech.2021-644 | 3445-3449 | @inproceedings{diwan21b_interspeech,
title = {{Reduce and Reconstruct: ASR for Low-Resource Phonetic Languages}},
author = {Anuj Diwan and Preethi Jyothi},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3445--3449},
doi = {10.21437/Interspeech.2021-644},
issn = {2958-... | This work presents a seemingly simple but effective technique to improve
low-resource ASR systems for phonetic languages. By identifying sets
of acoustically similar graphemes in these languages, we first reduce
the output alphabet of the ASR system using linguistically meaningful
reductions and then reconstruct the or... | 2010.09322 | title_snapshot |
fukuda21_interspeech | Knowledge Distillation Based Training of Universal ASR Source Models for Cross-Lingual Transfer | [
"Takashi Fukuda",
"Samuel Thomas"
] | https://www.isca-archive.org/interspeech_2021/fukuda21_interspeech.html | https://www.isca-archive.org/interspeech_2021/fukuda21_interspeech.pdf | 10.21437/Interspeech.2021-796 | 3450-3454 | @inproceedings{fukuda21_interspeech,
title = {{Knowledge Distillation Based Training of Universal ASR Source Models for Cross-Lingual Transfer}},
author = {Takashi Fukuda and Samuel Thomas},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3450--3454},
doi = {10.21437/Interspe... | In this paper we introduce a novel knowledge distillation based framework
for training universal source models. In our proposed approach for
automatic speech recognition (ASR), multilingual source models are
first trained using multiple language-dependent resources before being
used to initialize language specific targ... | null | null |
ray21_interspeech | Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End | [
"Swayambhu Nath Ray",
"Minhua Wu",
"Anirudh Raju",
"Pegah Ghahremani",
"Raghavendra Bilgi",
"Milind Rao",
"Harish Arsikere",
"Ariya Rastrow",
"Andreas Stolcke",
"Jasha Droppo"
] | https://www.isca-archive.org/interspeech_2021/ray21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ray21_interspeech.pdf | 10.21437/Interspeech.2021-836 | 3455-3459 | @inproceedings{ray21_interspeech,
title = {{Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End}},
author = {Swayambhu Nath Ray and Minhua Wu and Anirudh Raju and Pegah Ghahremani and Raghavendra Bilgi and Milind Rao and Harish Arsikere and Ariya Rastrow and Andreas Stolcke and Ja... | Comprehending the overall intent of an utterance helps a listener recognize
the individual words spoken. Inspired by this fact, we perform a novel
study of the impact of explicitly incorporating intent representations
as additional information to improve a recurrent neural network-transducer
(RNN-T) based automatic spe... | 2105.07071 | title_snapshot |
lu21c_interspeech | Exploring Targeted Universal Adversarial Perturbations to End-to-End ASR Models | [
"Zhiyun Lu",
"Wei Han",
"Yu Zhang",
"Liangliang Cao"
] | https://www.isca-archive.org/interspeech_2021/lu21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/lu21c_interspeech.pdf | 10.21437/Interspeech.2021-1668 | 3460-3464 | @inproceedings{lu21c_interspeech,
title = {{Exploring Targeted Universal Adversarial Perturbations to End-to-End ASR Models}},
author = {Zhiyun Lu and Wei Han and Yu Zhang and Liangliang Cao},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3460--3464},
doi = {10.21437/Inters... | Although end-to-end automatic speech recognition (e2e ASR) models are
widely deployed in many applications, there have been very few studies
to understand models’ robustness against adversarial perturbations.
In this paper, we explore whether a targeted universal perturbation
vector exists for e2e ASR models. Our goal ... | 2104.02757 | title_snapshot |
delrio21_interspeech | Earnings-21: A Practical Benchmark for ASR in the Wild | [
"Miguel Del Rio",
"Natalie Delworth",
"Ryan Westerman",
"Michelle Huang",
"Nishchal Bhandari",
"Joseph Palakapilly",
"Quinten McNamara",
"Joshua Dong",
"Piotr Żelasko",
"Miguel Jetté"
] | https://www.isca-archive.org/interspeech_2021/delrio21_interspeech.html | https://www.isca-archive.org/interspeech_2021/delrio21_interspeech.pdf | 10.21437/Interspeech.2021-1915 | 3465-3469 | @inproceedings{delrio21_interspeech,
title = {{Earnings-21: A Practical Benchmark for ASR in the Wild}},
author = {Miguel {Del Rio} and Natalie Delworth and Ryan Westerman and Michelle Huang and Nishchal Bhandari and Joseph Palakapilly and Quinten McNamara and Joshua Dong and Piotr Żelasko and Miguel Jetté},... | Commonly used speech corpora inadequately challenge academic and commercial
ASR systems. In particular, speech corpora lack metadata needed for
detailed analysis and WER measurement. In response, we present Earnings-21 ,
a 39-hour corpus of earnings calls containing entity-dense speech from
nine different financial sec... | 2104.11348 | title_snapshot |
sun21c_interspeech | Improving Multilingual Transformer Transducer Models by Reducing Language Confusions | [
"Eric Sun",
"Jinyu Li",
"Zhong Meng",
"Yu Wu",
"Jian Xue",
"Shujie Liu",
"Yifan Gong"
] | https://www.isca-archive.org/interspeech_2021/sun21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/sun21c_interspeech.pdf | 10.21437/Interspeech.2021-1949 | 3470-3474 | @inproceedings{sun21c_interspeech,
title = {{Improving Multilingual Transformer Transducer Models by Reducing Language Confusions}},
author = {Eric Sun and Jinyu Li and Zhong Meng and Yu Wu and Jian Xue and Shujie Liu and Yifan Gong},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3... | In end-to-end multilingual speech recognition, the hypotheses in one
language could include word tokens from other languages. Language confusions
happen even more frequently when language identifier (LID) is not present
during inference. In this paper, we explore to reduce language confusions
without using LID in model... | null | null |
ali21b_interspeech | Arabic Code-Switching Speech Recognition Using Monolingual Data | [
"Ahmed Ali",
"Shammur Absar Chowdhury",
"Amir Hussein",
"Yasser Hifny"
] | https://www.isca-archive.org/interspeech_2021/ali21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/ali21b_interspeech.pdf | 10.21437/Interspeech.2021-2231 | 3475-3479 | @inproceedings{ali21b_interspeech,
title = {{Arabic Code-Switching Speech Recognition Using Monolingual Data}},
author = {Ahmed Ali and Shammur Absar Chowdhury and Amir Hussein and Yasser Hifny},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3475--3479},
doi = {10.21437/Int... | Code-switching in automatic speech recognition (ASR) is an important
challenge due to globalization. Recent research in multilingual ASR
shows potential improvement over monolingual systems. We study key
issues related to multilingual modeling for ASR through a series of
large-scale ASR experiments. Our innovative fram... | 2107.01573 | title_snapshot |
eisenberg21_interspeech | Online Blind Audio Source Separation Using Recursive Expectation-Maximization | [
"Aviad Eisenberg",
"Boaz Schwartz",
"Sharon Gannot"
] | https://www.isca-archive.org/interspeech_2021/eisenberg21_interspeech.html | https://www.isca-archive.org/interspeech_2021/eisenberg21_interspeech.pdf | 10.21437/Interspeech.2021-662 | 3480-3484 | @inproceedings{eisenberg21_interspeech,
title = {{Online Blind Audio Source Separation Using Recursive Expectation-Maximization}},
author = {Aviad Eisenberg and Boaz Schwartz and Sharon Gannot},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3480--3484},
doi = {10.21437/Inte... | The challenging problem of online multi-microphone blind audio source
separation (BASS) in noisy environment is addressed in this paper.
We present a sequential, non-iterative, algorithm based on the recursive
EM (REM) framework. In the proposed algorithm, the compete-data, which
constitutes the separated sources and r... | null | null |
luo21e_interspeech | Empirical Analysis of Generalized Iterative Speech Separation Networks | [
"Yi Luo",
"Cong Han",
"Nima Mesgarani"
] | https://www.isca-archive.org/interspeech_2021/luo21e_interspeech.html | https://www.isca-archive.org/interspeech_2021/luo21e_interspeech.pdf | 10.21437/Interspeech.2021-1161 | 3485-3489 | @inproceedings{luo21e_interspeech,
title = {{Empirical Analysis of Generalized Iterative Speech Separation Networks}},
author = {Yi Luo and Cong Han and Nima Mesgarani},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3485--3489},
doi = {10.21437/Interspeech.2021-1161},
iss... | Although most existing speech separation networks are designed as a
one-pass pipeline where the sources are directly estimated from the
mixture, multi-pass or iterative pipelines have been shown to be effective
by designing multiple rounds of separation and utilizing separation
outputs from a previous iteration as addi... | null | null |
neumann21_interspeech | Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers | [
"Thilo von Neumann",
"Keisuke Kinoshita",
"Christoph Boeddeker",
"Marc Delcroix",
"Reinhold Haeb-Umbach"
] | https://www.isca-archive.org/interspeech_2021/neumann21_interspeech.html | https://www.isca-archive.org/interspeech_2021/neumann21_interspeech.pdf | 10.21437/Interspeech.2021-1177 | 3490-3494 | @inproceedings{neumann21_interspeech,
title = {{Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers}},
author = {Thilo {von Neumann} and Keisuke Kinoshita and Christoph Boeddeker and Marc Delcroix and Reinhold Haeb-Umbach},
year = {2021},
... | Automatic transcription of meetings requires handling of overlapped
speech, which calls for continuous speech separation (CSS) systems.
The uPIT criterion was proposed for utterance-level separation with
neural networks and introduces the constraint that the total number
of speakers must not exceed the number of output... | 2107.14446 | title_snapshot |
zhang21v_interspeech | Teacher-Student MixIT for Unsupervised and Semi-Supervised Speech Separation | [
"Jisi Zhang",
"Cătălin Zorilă",
"Rama Doddipatla",
"Jon Barker"
] | https://www.isca-archive.org/interspeech_2021/zhang21v_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21v_interspeech.pdf | 10.21437/Interspeech.2021-1243 | 3495-3499 | @inproceedings{zhang21v_interspeech,
title = {{Teacher-Student MixIT for Unsupervised and Semi-Supervised Speech Separation}},
author = {Jisi Zhang and Cătălin Zorilă and Rama Doddipatla and Jon Barker},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3495--3499},
doi = {10.2... | In this paper, we introduce a novel semi-supervised learning framework
for end-to-end speech separation. The proposed method first uses mixtures
of unseparated sources and the mixture invariant training (MixIT) criterion
to train a teacher model. The teacher model then estimates separated
sources that are used to train... | 2106.07843 | title_snapshot |
delcroix21_interspeech | Few-Shot Learning of New Sound Classes for Target Sound Extraction | [
"Marc Delcroix",
"Jorge Bennasar Vázquez",
"Tsubasa Ochiai",
"Keisuke Kinoshita",
"Shoko Araki"
] | https://www.isca-archive.org/interspeech_2021/delcroix21_interspeech.html | https://www.isca-archive.org/interspeech_2021/delcroix21_interspeech.pdf | 10.21437/Interspeech.2021-1369 | 3500-3504 | @inproceedings{delcroix21_interspeech,
title = {{Few-Shot Learning of New Sound Classes for Target Sound Extraction}},
author = {Marc Delcroix and Jorge Bennasar Vázquez and Tsubasa Ochiai and Keisuke Kinoshita and Shoko Araki},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3500--3... | Target sound extraction consists of extracting the sound of a target
acoustic event (AE) class from a mixture of AE sounds. It can be realized
using a neural network that extracts the target sound conditioned on
a 1-hot vector that represents the desired AE class. With this approach,
embedding vectors associated with t... | 2106.07144 | title_snapshot |
han21e_interspeech | Binaural Speech Separation of Moving Speakers With Preserved Spatial Cues | [
"Cong Han",
"Yi Luo",
"Nima Mesgarani"
] | https://www.isca-archive.org/interspeech_2021/han21e_interspeech.html | https://www.isca-archive.org/interspeech_2021/han21e_interspeech.pdf | 10.21437/Interspeech.2021-1372 | 3505-3509 | @inproceedings{han21e_interspeech,
title = {{Binaural Speech Separation of Moving Speakers With Preserved Spatial Cues}},
author = {Cong Han and Yi Luo and Nima Mesgarani},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3505--3509},
doi = {10.21437/Interspeech.2021-1372},
... | Binaural speech separation algorithms designed for augmented hearing
technologies need to both improve the signal-to-noise ratio of individual
speakers and preserve their perceived location in space. The majority
of binaural speech separation methods assume nonmoving speakers. As
a result, their application to real-wor... | null | null |
hu21_interspeech | AvaTr: One-Shot Speaker Extraction with Transformers | [
"Shell Xu Hu",
"Md. Rifat Arefin",
"Viet-Nhat Nguyen",
"Alish Dipani",
"Xaq Pitkow",
"Andreas Savas Tolias"
] | https://www.isca-archive.org/interspeech_2021/hu21_interspeech.html | https://www.isca-archive.org/interspeech_2021/hu21_interspeech.pdf | 10.21437/Interspeech.2021-1378 | 3510-3514 | @inproceedings{hu21_interspeech,
title = {{AvaTr: One-Shot Speaker Extraction with Transformers}},
author = {Shell Xu Hu and Md. Rifat Arefin and Viet-Nhat Nguyen and Alish Dipani and Xaq Pitkow and Andreas Savas Tolias},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3510--3514},
... | To extract the voice of a target speaker when mixed with a variety
of other sounds, such as white and ambient noises or the voices of
interfering speakers, we extend the Transformer network [1] to attend
the most relevant information with respect to the target speaker given
the characteristics of his or her voices as a... | 2105.00609 | title_snapshot |
sarkar21_interspeech | Vocal Harmony Separation Using Time-Domain Neural Networks | [
"Saurjya Sarkar",
"Emmanouil Benetos",
"Mark Sandler"
] | https://www.isca-archive.org/interspeech_2021/sarkar21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sarkar21_interspeech.pdf | 10.21437/Interspeech.2021-1531 | 3515-3519 | @inproceedings{sarkar21_interspeech,
title = {{Vocal Harmony Separation Using Time-Domain Neural Networks}},
author = {Saurjya Sarkar and Emmanouil Benetos and Mark Sandler},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3515--3519},
doi = {10.21437/Interspeech.2021-1531},
... | Polyphonic vocal recordings are an inherently challenging source separation
task due to the melodic structure of the vocal parts and unique timbre
of its constituents. In this work we utilise a time-domain neural network
architecture re-purposed from speech separation research and modify
it to separate a capella mixtur... | null | null |
maciejewski21_interspeech | Speaker Verification-Based Evaluation of Single-Channel Speech Separation | [
"Matthew Maciejewski",
"Shinji Watanabe",
"Sanjeev Khudanpur"
] | https://www.isca-archive.org/interspeech_2021/maciejewski21_interspeech.html | https://www.isca-archive.org/interspeech_2021/maciejewski21_interspeech.pdf | 10.21437/Interspeech.2021-1924 | 3520-3524 | @inproceedings{maciejewski21_interspeech,
title = {{Speaker Verification-Based Evaluation of Single-Channel Speech Separation}},
author = {Matthew Maciejewski and Shinji Watanabe and Sanjeev Khudanpur},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3520--3524},
doi = {10.21... | Speech enhancement techniques typically focus on intrinsic metrics
of signal quality. The overwhelming majority of deep learning-based
single-channel speech separation studies, for instance, have relied
on a single class of metrics to evaluate the systems by. These metrics,
usually variants of Signal-to-Distortion Rati... | null | null |
lan21_interspeech | Improved Speech Separation with Time-and-Frequency Cross-Domain Feature Selection | [
"Tian Lan",
"Yuxin Qian",
"Yilan Lyu",
"Refuoe Mokhosi",
"Wenxin Tai",
"Qiao Liu"
] | https://www.isca-archive.org/interspeech_2021/lan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/lan21_interspeech.pdf | 10.21437/Interspeech.2021-2246 | 3525-3529 | @inproceedings{lan21_interspeech,
title = {{Improved Speech Separation with Time-and-Frequency Cross-Domain Feature Selection}},
author = {Tian Lan and Yuxin Qian and Yilan Lyu and Refuoe Mokhosi and Wenxin Tai and Qiao Liu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3525--3529... | Most deep learning-based monaural speech separation models only use
either spectrograms or time domain speech signal as the input feature.
The recently proposed cross-domain network (CDNet) demonstrates that
concatenated frequency domain and time domain features helps to reach
better performance. Although concatenation... | null | null |
deng21c_interspeech | Robust Speaker Extraction Network Based on Iterative Refined Adaptation | [
"Chengyun Deng",
"Shiqian Ma",
"Yongtao Sha",
"Yi Zhang",
"Hui Zhang",
"Hui Song",
"Fei Wang"
] | https://www.isca-archive.org/interspeech_2021/deng21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/deng21c_interspeech.pdf | 10.21437/Interspeech.2021-2250 | 3530-3534 | @inproceedings{deng21c_interspeech,
title = {{Robust Speaker Extraction Network Based on Iterative Refined Adaptation}},
author = {Chengyun Deng and Shiqian Ma and Yongtao Sha and Yi Zhang and Hui Zhang and Hui Song and Fei Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3530-... | Speaker extraction aims to extract target speech signal from a multi-talker
environment with interference speakers and surrounding noise, given
a reference speech from target speaker. Most speaker extraction systems
achieve satisfactory performance in the closed condition. Such systems
suffer from performance degradati... | 2011.02102 | title_snapshot |
wang21aa_interspeech | Neural Speaker Extraction with Speaker-Speech Cross-Attention Network | [
"Wupeng Wang",
"Chenglin Xu",
"Meng Ge",
"Haizhou Li"
] | https://www.isca-archive.org/interspeech_2021/wang21aa_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21aa_interspeech.pdf | 10.21437/Interspeech.2021-2260 | 3535-3539 | @inproceedings{wang21aa_interspeech,
title = {{Neural Speaker Extraction with Speaker-Speech Cross-Attention Network}},
author = {Wupeng Wang and Chenglin Xu and Meng Ge and Haizhou Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3535--3539},
doi = {10.21437/Interspeech.... | In this paper, we propose a novel time-domain speaker-speech cross-attention
network as a variant of SpEx [1] architecture, that features speaker-speech
cross-attention. The speaker-speech cross-attention network consists
of speech semantic layers that capture the high-level dependency of
audio feature, and cross-atten... | null | null |
rigal21_interspeech | Deep Audio-Visual Speech Separation Based on Facial Motion | [
"Rémi Rigal",
"Jacques Chodorowski",
"Benoît Zerr"
] | https://www.isca-archive.org/interspeech_2021/rigal21_interspeech.html | https://www.isca-archive.org/interspeech_2021/rigal21_interspeech.pdf | 10.21437/Interspeech.2021-1560 | 3540-3544 | @inproceedings{rigal21_interspeech,
title = {{Deep Audio-Visual Speech Separation Based on Facial Motion}},
author = {Rémi Rigal and Jacques Chodorowski and Benoît Zerr},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3540--3544},
doi = {10.21437/Interspeech.2021-1560},
is... | We present a deep neural network that relies on facial motion and time-domain
audio for isolating speech signals from a mixture of speeches and background
noises. Recent studies in deep learning-based audio-visual speech separation
and speech enhancement have proven that leveraging visual information
in addition to aud... | null | null |
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