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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|---|---|---|---|---|---|---|---|---|---|---|
swaminathan21_interspeech | CoDERT: Distilling Encoder Representations with Co-Learning for Transducer-Based Speech Recognition | [
"Rupak Vignesh Swaminathan",
"Brian King",
"Grant P. Strimel",
"Jasha Droppo",
"Athanasios Mouchtaris"
] | https://www.isca-archive.org/interspeech_2021/swaminathan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/swaminathan21_interspeech.pdf | 10.21437/Interspeech.2021-797 | 4543-4547 | @inproceedings{swaminathan21_interspeech,
title = {{CoDERT: Distilling Encoder Representations with Co-Learning for Transducer-Based Speech Recognition}},
author = {Rupak Vignesh Swaminathan and Brian King and Grant P. Strimel and Jasha Droppo and Athanasios Mouchtaris},
year = {2021},
booktitle = {... | We propose a simple yet effective method to compress an RNN-Transducer
(RNN-T) through the well-known knowledge distillation paradigm. We
show that the transducer’s encoder outputs naturally have a high
entropy and contain rich information about acoustically similar word-piece
confusions. This rich information is suppr... | 2106.07734 | title_snapshot |
gao21f_interspeech | Extremely Low Footprint End-to-End ASR System for Smart Device | [
"Zhifu Gao",
"Yiwu Yao",
"Shiliang Zhang",
"Jun Yang",
"Ming Lei",
"Ian McLoughlin"
] | https://www.isca-archive.org/interspeech_2021/gao21f_interspeech.html | https://www.isca-archive.org/interspeech_2021/gao21f_interspeech.pdf | 10.21437/Interspeech.2021-819 | 4548-4552 | @inproceedings{gao21f_interspeech,
title = {{Extremely Low Footprint End-to-End ASR System for Smart Device}},
author = {Zhifu Gao and Yiwu Yao and Shiliang Zhang and Jun Yang and Ming Lei and Ian McLoughlin},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4548--4552},
doi =... | Recently, end-to-end (E2E) speech recognition has become popular, since
it can integrate the acoustic, pronunciation and language models into
a single neural network, which outperforms conventional models. Among
E2E approaches, attention-based models, e.g. Transformer, have emerged
as being superior. Such models have o... | 2104.05784 | title_snapshot |
shangguan21_interspeech | Dissecting User-Perceived Latency of On-Device E2E Speech Recognition | [
"Yuan Shangguan",
"Rohit Prabhavalkar",
"Hang Su",
"Jay Mahadeokar",
"Yangyang Shi",
"Jiatong Zhou",
"Chunyang Wu",
"Duc Le",
"Ozlem Kalinli",
"Christian Fuegen",
"Michael L. Seltzer"
] | https://www.isca-archive.org/interspeech_2021/shangguan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/shangguan21_interspeech.pdf | 10.21437/Interspeech.2021-1887 | 4553-4557 | @inproceedings{shangguan21_interspeech,
title = {{Dissecting User-Perceived Latency of On-Device E2E Speech Recognition}},
author = {Yuan Shangguan and Rohit Prabhavalkar and Hang Su and Jay Mahadeokar and Yangyang Shi and Jiatong Zhou and Chunyang Wu and Duc Le and Ozlem Kalinli and Christian Fuegen and Mic... | As speech-enabled devices such as smartphones and smart speakers become
increasingly ubiquitous, there is growing interest in building automatic
speech recognition (ASR) systems that can run directly on-device; end-to-end
(E2E) speech recognition models such as recurrent neural network transducers
and their variants ha... | 2104.02207 | title_snapshot |
macoskey21b_interspeech | Amortized Neural Networks for Low-Latency Speech Recognition | [
"Jonathan Macoskey",
"Grant P. Strimel",
"Jinru Su",
"Ariya Rastrow"
] | https://www.isca-archive.org/interspeech_2021/macoskey21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/macoskey21b_interspeech.pdf | 10.21437/Interspeech.2021-712 | 4558-4562 | @inproceedings{macoskey21b_interspeech,
title = {{Amortized Neural Networks for Low-Latency Speech Recognition}},
author = {Jonathan Macoskey and Grant P. Strimel and Jinru Su and Ariya Rastrow},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4558--4562},
doi = {10.21437/Int... | We introduce Amortized Neural Networks (AmNets), a compute cost- and
latency-aware network architecture particularly well-suited for sequence
modeling tasks. We apply AmNets to the Recurrent Neural Network Transducer
(RNN-T) to reduce compute cost and latency for an automatic speech
recognition (ASR) task. The AmNets R... | 2108.01553 | title_snapshot |
botros21_interspeech | Tied & Reduced RNN-T Decoder | [
"Rami Botros",
"Tara N. Sainath",
"Robert David",
"Emmanuel Guzman",
"Wei Li",
"Yanzhang He"
] | https://www.isca-archive.org/interspeech_2021/botros21_interspeech.html | https://www.isca-archive.org/interspeech_2021/botros21_interspeech.pdf | 10.21437/Interspeech.2021-212 | 4563-4567 | @inproceedings{botros21_interspeech,
title = {{Tied & Reduced RNN-T Decoder}},
author = {Rami Botros and Tara N. Sainath and Robert David and Emmanuel Guzman and Wei Li and Yanzhang He},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4563--4567},
doi = {10.21437/Interspeech.... | Previous works on the Recurrent Neural Network-Transducer (RNN-T) models
have shown that, under some conditions, it is possible to simplify
its prediction network with little or no loss in recognition accuracy
[1, 2, 3]. This is done by limiting the context size of previous labels
and/or using a simpler architecture fo... | 2109.07513 | title_snapshot |
kim21m_interspeech | PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation | [
"Jangho Kim",
"Simyung Chang",
"Nojun Kwak"
] | https://www.isca-archive.org/interspeech_2021/kim21m_interspeech.html | https://www.isca-archive.org/interspeech_2021/kim21m_interspeech.pdf | 10.21437/Interspeech.2021-248 | 4568-4572 | @inproceedings{kim21m_interspeech,
title = {{PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation}},
author = {Jangho Kim and Simyung Chang and Nojun Kwak},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4568--4572},
doi = {10.21437/Interspeech.2021-2... | As edge devices become prevalent, deploying Deep Neural Networks (DNN)
on edge devices has become a critical issue. However, DNN requires
a high computational resource which is rarely available for edge devices.
To handle this, we propose a novel model compression method for the
devices with limited computational resou... | 2106.14681 | title_snapshot |
nagaraja21_interspeech | Collaborative Training of Acoustic Encoders for Speech Recognition | [
"Varun Nagaraja",
"Yangyang Shi",
"Ganesh Venkatesh",
"Ozlem Kalinli",
"Michael L. Seltzer",
"Vikas Chandra"
] | https://www.isca-archive.org/interspeech_2021/nagaraja21_interspeech.html | https://www.isca-archive.org/interspeech_2021/nagaraja21_interspeech.pdf | 10.21437/Interspeech.2021-354 | 4573-4577 | @inproceedings{nagaraja21_interspeech,
title = {{Collaborative Training of Acoustic Encoders for Speech Recognition}},
author = {Varun Nagaraja and Yangyang Shi and Ganesh Venkatesh and Ozlem Kalinli and Michael L. Seltzer and Vikas Chandra},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | On-device speech recognition requires training models of different
sizes for deploying on devices with various computational budgets.
When building such different models, we can benefit from training them
jointly to take advantage of the knowledge shared between them. Joint
training is also efficient since it reduces t... | 2106.08960 | title_snapshot |
wang21ha_interspeech | Efficient Conformer with Prob-Sparse Attention Mechanism for End-to-End Speech Recognition | [
"Xiong Wang",
"Sining Sun",
"Lei Xie",
"Long Ma"
] | https://www.isca-archive.org/interspeech_2021/wang21ha_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21ha_interspeech.pdf | 10.21437/Interspeech.2021-415 | 4578-4582 | @inproceedings{wang21ha_interspeech,
title = {{Efficient Conformer with Prob-Sparse Attention Mechanism for End-to-End Speech Recognition}},
author = {Xiong Wang and Sining Sun and Lei Xie and Long Ma},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4578--4582},
doi = {10.21... | End-to-end models are favored in automatic speech recognition (ASR)
because of their simplified system structure and superior performance.
Among these models, Transformer and Conformer have achieved state-of-the-art
recognition accuracy in which self-attention plays a vital role in
capturing important global informatio... | 2106.09236 | title_judge |
parcollet21_interspeech | The Energy and Carbon Footprint of Training End-to-End Speech Recognizers | [
"Titouan Parcollet",
"Mirco Ravanelli"
] | https://www.isca-archive.org/interspeech_2021/parcollet21_interspeech.html | https://www.isca-archive.org/interspeech_2021/parcollet21_interspeech.pdf | 10.21437/Interspeech.2021-456 | 4583-4587 | @inproceedings{parcollet21_interspeech,
title = {{The Energy and Carbon Footprint of Training End-to-End Speech Recognizers}},
author = {Titouan Parcollet and Mirco Ravanelli},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4583--4587},
doi = {10.21437/Interspeech.2021-456},... | Deep learning contributes to reaching higher levels of artificial intelligence.
Due to its pervasive adoption, however, growing concerns on the environmental
impact of this technology have been raised. In particular, the energy
consumed at training and inference time by modern neural networks is
far from being negligib... | null | null |
chen21v_interspeech | Graph-Based Label Propagation for Semi-Supervised Speaker Identification | [
"Long Chen",
"Venkatesh Ravichandran",
"Andreas Stolcke"
] | https://www.isca-archive.org/interspeech_2021/chen21v_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21v_interspeech.pdf | 10.21437/Interspeech.2021-1209 | 4588-4592 | @inproceedings{chen21v_interspeech,
title = {{Graph-Based Label Propagation for Semi-Supervised Speaker Identification}},
author = {Long Chen and Venkatesh Ravichandran and Andreas Stolcke},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4588--4592},
doi = {10.21437/Interspe... | Speaker identification in the household scenario (e.g., for smart speakers)
is typically based on only a few enrollment utterances but a much larger
set of unlabeled data, suggesting semi-supervised learning to improve
speaker profiles. We propose a graph-based semi-supervised learning
approach for speaker identificati... | 2106.08207 | title_snapshot |
li21q_interspeech | Fusion of Embeddings Networks for Robust Combination of Text Dependent and Independent Speaker Recognition | [
"Ruirui Li",
"Chelsea J.-T. Ju",
"Zeya Chen",
"Hongda Mao",
"Oguz Elibol",
"Andreas Stolcke"
] | https://www.isca-archive.org/interspeech_2021/li21q_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21q_interspeech.pdf | 10.21437/Interspeech.2021-3 | 4593-4597 | @inproceedings{li21q_interspeech,
title = {{Fusion of Embeddings Networks for Robust Combination of Text Dependent and Independent Speaker Recognition}},
author = {Ruirui Li and Chelsea J.-T. Ju and Zeya Chen and Hongda Mao and Oguz Elibol and Andreas Stolcke},
year = {2021},
booktitle = {{Interspee... | By implicitly recognizing a user based on his/her speech input, speaker
identification enables many downstream applications, such as personalized
system behavior and expedited shopping checkouts. Based on whether
the speech content is constrained or not, both text-dependent (TD)
and text-independent (TI) speaker recogn... | 2106.10169 | title_snapshot |
cumani21_interspeech | A Generative Model for Duration-Dependent Score Calibration | [
"Sandro Cumani",
"Salvatore Sarni"
] | https://www.isca-archive.org/interspeech_2021/cumani21_interspeech.html | https://www.isca-archive.org/interspeech_2021/cumani21_interspeech.pdf | 10.21437/Interspeech.2021-114 | 4598-4602 | @inproceedings{cumani21_interspeech,
title = {{A Generative Model for Duration-Dependent Score Calibration}},
author = {Sandro Cumani and Salvatore Sarni},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4598--4602},
doi = {10.21437/Interspeech.2021-114},
issn = {2958-... | In this work we introduce a generative score calibration model for
speaker verification systems able to explicitly account for utterance-dependent
miscalibration sources, with a focus on segment duration. The model
is theoretically motivated by an analysis of the effects of distribution
mismatch on the scores produced ... | null | null |
pelecanos21_interspeech | Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognition | [
"Jason Pelecanos",
"Quan Wang",
"Ignacio Lopez Moreno"
] | https://www.isca-archive.org/interspeech_2021/pelecanos21_interspeech.html | https://www.isca-archive.org/interspeech_2021/pelecanos21_interspeech.pdf | 10.21437/Interspeech.2021-641 | 4603-4607 | @inproceedings{pelecanos21_interspeech,
title = {{Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognition}},
author = {Jason Pelecanos and Quan Wang and Ignacio Lopez Moreno},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4603--4607},
doi = {10.2... | Many neural network speaker recognition systems model each speaker
using a fixed-dimensional embedding vector. These embeddings are generally
compared using either linear or 2nd-order scoring and, until recently,
do not handle utterance-specific uncertainty. In this work we propose
scoring these representations in a wa... | 2104.01989 | title_snapshot |
kataria21b_interspeech | Multi-Channel Speaker Verification for Single and Multi-Talker Speech | [
"Saurabh Kataria",
"Shi-Xiong Zhang",
"Dong Yu"
] | https://www.isca-archive.org/interspeech_2021/kataria21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/kataria21b_interspeech.pdf | 10.21437/Interspeech.2021-681 | 4608-4612 | @inproceedings{kataria21b_interspeech,
title = {{Multi-Channel Speaker Verification for Single and Multi-Talker Speech}},
author = {Saurabh Kataria and Shi-Xiong Zhang and Dong Yu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4608--4612},
doi = {10.21437/Interspeech.2021-... | To improve speaker verification in real scenarios with interference
speakers, noise, and reverberation, we propose to bring together advancements
made in multi-channel speech features. Specifically, we combine spectral , spatial , and directional features, which includes inter-channel
phase difference, multichannel sin... | 2010.12692 | title_snapshot |
padfield21_interspeech | Chronological Self-Training for Real-Time Speaker Diarization | [
"Dirk Padfield",
"Daniel J. Liebling"
] | https://www.isca-archive.org/interspeech_2021/padfield21_interspeech.html | https://www.isca-archive.org/interspeech_2021/padfield21_interspeech.pdf | 10.21437/Interspeech.2021-822 | 4613-4617 | @inproceedings{padfield21_interspeech,
title = {{Chronological Self-Training for Real-Time Speaker Diarization}},
author = {Dirk Padfield and Daniel J. Liebling},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4613--4617},
doi = {10.21437/Interspeech.2021-822},
issn =... | Diarization partitions an audio stream into segments based on the voices
of the speakers. Real-time diarization systems that include an enrollment
step should limit enrollment training samples to reduce user interaction
time. Although training on a small number of samples yields poor performance,
we show that the accur... | 2208.03393 | title_snapshot |
xiao21b_interspeech | Adaptive Margin Circle Loss for Speaker Verification | [
"Runqiu Xiao",
"Xiaoxiao Miao",
"Wenchao Wang",
"Pengyuan Zhang",
"Bin Cai",
"Liuping Luo"
] | https://www.isca-archive.org/interspeech_2021/xiao21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/xiao21b_interspeech.pdf | 10.21437/Interspeech.2021-1043 | 4618-4622 | @inproceedings{xiao21b_interspeech,
title = {{Adaptive Margin Circle Loss for Speaker Verification}},
author = {Runqiu Xiao and Xiaoxiao Miao and Wenchao Wang and Pengyuan Zhang and Bin Cai and Liuping Luo},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4618--4622},
doi = {... | Deep-Neural-Network (DNN) based speaker verification systems use the
angular softmax loss with margin penalties to enhance the intra-class
compactness of speaker embeddings, which achieved remarkable performance.
In this paper, we propose a novel angular loss function called adaptive
margin circle loss for speaker veri... | 2106.08004 | title_snapshot |
obrien21b_interspeech | Presentation Matters: Evaluating Speaker Identification Tasks | [
"Benjamin O’Brien",
"Christine Meunier",
"Alain Ghio"
] | https://www.isca-archive.org/interspeech_2021/obrien21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/obrien21b_interspeech.pdf | 10.21437/Interspeech.2021-1211 | 4623-4627 | @inproceedings{obrien21b_interspeech,
title = {{Presentation Matters: Evaluating Speaker Identification Tasks}},
author = {Benjamin O’Brien and Christine Meunier and Alain Ghio},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4623--4627},
doi = {10.21437/Interspeech.2021-121... | This paper details our evaluations and comparisons of speaker identification
(SID) performance by listeners across different tasks. Experiment 1
participants completed traditional target-lineup (1-out-of-N speakers
or out-of-set speaker) and binary (speaker verification) tasks. Experiment
2 participants completed trial... | null | null |
tong21_interspeech | Automatic Error Correction for Speaker Embedding Learning with Noisy Labels | [
"Fuchuan Tong",
"Yan Liu",
"Song Li",
"Jie Wang",
"Lin Li",
"Qingyang Hong"
] | https://www.isca-archive.org/interspeech_2021/tong21_interspeech.html | https://www.isca-archive.org/interspeech_2021/tong21_interspeech.pdf | 10.21437/Interspeech.2021-2021 | 4628-4632 | @inproceedings{tong21_interspeech,
title = {{Automatic Error Correction for Speaker Embedding Learning with Noisy Labels}},
author = {Fuchuan Tong and Yan Liu and Song Li and Jie Wang and Lin Li and Qingyang Hong},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4628--4632},
doi ... | Despite the superior performance deep neural networks have achieved
in speaker verification tasks, much of their success benefits from
the availability of large-scale and carefully labeled datasets. However,
noisy labels often occur during data collection. In this paper, we
propose an automatic error correction method ... | null | null |
liao21_interspeech | An Integrated Framework for Two-Pass Personalized Voice Trigger | [
"Dexin Liao",
"Jing Li",
"Yiming Zhi",
"Song Li",
"Qingyang Hong",
"Lin Li"
] | https://www.isca-archive.org/interspeech_2021/liao21_interspeech.html | https://www.isca-archive.org/interspeech_2021/liao21_interspeech.pdf | 10.21437/Interspeech.2021-2161 | 4633-4637 | @inproceedings{liao21_interspeech,
title = {{An Integrated Framework for Two-Pass Personalized Voice Trigger}},
author = {Dexin Liao and Jing Li and Yiming Zhi and Song Li and Qingyang Hong and Lin Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4633--4637},
doi = {10.21... | In this paper, we present the XMUSPEECH system for Task 1 of 2020 Personalized
Voice Trigger Challenge (PVTC2020). Task 1 is a joint wake-up word
detection with speaker verification on close talking data. The whole
system consists of a keyword spotting (KWS) sub-system and a speaker
verification (SV) sub-system. For th... | 2106.15950 | title_snapshot |
lian21_interspeech | Masked Proxy Loss for Text-Independent Speaker Verification | [
"Jiachen Lian",
"Aiswarya Vinod Kumar",
"Hira Dhamyal",
"Bhiksha Raj",
"Rita Singh"
] | https://www.isca-archive.org/interspeech_2021/lian21_interspeech.html | https://www.isca-archive.org/interspeech_2021/lian21_interspeech.pdf | 10.21437/Interspeech.2021-2190 | 4638-4642 | @inproceedings{lian21_interspeech,
title = {{Masked Proxy Loss for Text-Independent Speaker Verification}},
author = {Jiachen Lian and Aiswarya Vinod Kumar and Hira Dhamyal and Bhiksha Raj and Rita Singh},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4638--4642},
doi = {10... | Open-set speaker recognition can be regarded as a metric learning problem,
which is to maximize inter-class variance and minimize intra-class
variance. Supervised metric learning can be categorized into pair-based
learning and proxy-based learning [1]. Most of the existing metric
learning objectives belong to the forme... | 2011.04491 | title_snapshot |
lee21h_interspeech | STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech | [
"Keon Lee",
"Kyumin Park",
"Daeyoung Kim"
] | https://www.isca-archive.org/interspeech_2021/lee21h_interspeech.html | https://www.isca-archive.org/interspeech_2021/lee21h_interspeech.pdf | 10.21437/Interspeech.2021-838 | 4643-4647 | @inproceedings{lee21h_interspeech,
title = {{STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech}},
author = {Keon Lee and Kyumin Park and Daeyoung Kim},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | Previous works on neural text-to-speech (TTS) have been addressed on
limited speed in training and inference time, robustness for difficult
synthesis conditions, expressiveness, and controllability. Although
several approaches resolve some limitations, there has been no attempt
to solve all weaknesses at once. In this ... | 2103.09474 | title_snapshot |
liu21p_interspeech | Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability | [
"Rui Liu",
"Berrak Sisman",
"Haizhou Li"
] | https://www.isca-archive.org/interspeech_2021/liu21p_interspeech.html | https://www.isca-archive.org/interspeech_2021/liu21p_interspeech.pdf | 10.21437/Interspeech.2021-1236 | 4648-4652 | @inproceedings{liu21p_interspeech,
title = {{Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability}},
author = {Rui Liu and Berrak Sisman and Haizhou Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4648--4652},
doi = {10.214... | Emotional text-to-speech synthesis (ETTS) has seen much progress in
recent years. However, the generated voice is often not perceptually
identifiable by its intended emotion category. To address this problem,
we propose a new interactive training paradigm for ETTS, denoted as i-ETTS , which seeks to directly improve th... | 2104.01408 | title_snapshot |
sivaprasad21_interspeech | Emotional Prosody Control for Speech Generation | [
"Sarath Sivaprasad",
"Saiteja Kosgi",
"Vineet Gandhi"
] | https://www.isca-archive.org/interspeech_2021/sivaprasad21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sivaprasad21_interspeech.pdf | 10.21437/Interspeech.2021-307 | 4653-4657 | @inproceedings{sivaprasad21_interspeech,
title = {{Emotional Prosody Control for Speech Generation}},
author = {Sarath Sivaprasad and Saiteja Kosgi and Vineet Gandhi},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4653--4657},
doi = {10.21437/Interspeech.2021-307},
issn ... | Machine-generated speech is characterized by its limited or unnatural
emotional variation. Current text to speech systems generates speech
with either a flat emotion, emotion selected from a predefined set,
average variation learned from prosody sequences in training data or
transferred from a source style. We propose ... | 2111.04730 | title_snapshot |
cong21b_interspeech | Controllable Context-Aware Conversational Speech Synthesis | [
"Jian Cong",
"Shan Yang",
"Na Hu",
"Guangzhi Li",
"Lei Xie",
"Dan Su"
] | https://www.isca-archive.org/interspeech_2021/cong21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/cong21b_interspeech.pdf | 10.21437/Interspeech.2021-412 | 4658-4662 | @inproceedings{cong21b_interspeech,
title = {{Controllable Context-Aware Conversational Speech Synthesis}},
author = {Jian Cong and Shan Yang and Na Hu and Guangzhi Li and Lei Xie and Dan Su},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4658--4662},
doi = {10.21437/Inters... | In spoken conversations, spontaneous behaviors like filled pause and
prolongations always happen. Conversational partner tends to align
features of their speech with their interlocutor which is known as
entrainment. To produce human-like conversations, we propose a unified
controllable spontaneous conversational speech... | 2106.10828 | title_snapshot |
kim21n_interspeech | Expressive Text-to-Speech Using Style Tag | [
"Minchan Kim",
"Sung Jun Cheon",
"Byoung Jin Choi",
"Jong Jin Kim",
"Nam Soo Kim"
] | https://www.isca-archive.org/interspeech_2021/kim21n_interspeech.html | https://www.isca-archive.org/interspeech_2021/kim21n_interspeech.pdf | 10.21437/Interspeech.2021-465 | 4663-4667 | @inproceedings{kim21n_interspeech,
title = {{Expressive Text-to-Speech Using Style Tag}},
author = {Minchan Kim and Sung Jun Cheon and Byoung Jin Choi and Jong Jin Kim and Nam Soo Kim},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4663--4667},
doi = {10.21437/Interspeech.2... | As recent text-to-speech (TTS) systems have been rapidly improved in
speech quality and generation speed, many researchers now focus on
a more challenging issue: expressive TTS. To control speaking styles,
existing expressive TTS models use categorical style index or reference
speech as style input. In this work, we pr... | 2104.00436 | title_snapshot |
yan21d_interspeech | Adaptive Text to Speech for Spontaneous Style | [
"Yuzi Yan",
"Xu Tan",
"Bohan Li",
"Guangyan Zhang",
"Tao Qin",
"Sheng Zhao",
"Yuan Shen",
"Wei-Qiang Zhang",
"Tie-Yan Liu"
] | https://www.isca-archive.org/interspeech_2021/yan21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/yan21d_interspeech.pdf | 10.21437/Interspeech.2021-584 | 4668-4672 | @inproceedings{yan21d_interspeech,
title = {{Adaptive Text to Speech for Spontaneous Style}},
author = {Yuzi Yan and Xu Tan and Bohan Li and Guangyan Zhang and Tao Qin and Sheng Zhao and Yuan Shen and Wei-Qiang Zhang and Tie-Yan Liu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4... | While recent text to speech (TTS) models perform very well in synthesizing
reading-style (e.g., audiobook) speech, it is still challenging to
synthesize spontaneous-style speech (e.g., podcast or conversation),
mainly because of two reasons: 1) the lack of training data for spontaneous
speech; 2) the difficulty in mode... | 2107.02530 | title_judge |
li21r_interspeech | Towards Multi-Scale Style Control for Expressive Speech Synthesis | [
"Xiang Li",
"Changhe Song",
"Jingbei Li",
"Zhiyong Wu",
"Jia Jia",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2021/li21r_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21r_interspeech.pdf | 10.21437/Interspeech.2021-947 | 4673-4677 | @inproceedings{li21r_interspeech,
title = {{Towards Multi-Scale Style Control for Expressive Speech Synthesis}},
author = {Xiang Li and Changhe Song and Jingbei Li and Zhiyong Wu and Jia Jia and Helen Meng},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4673--4677},
doi = {... | This paper introduces a multi-scale speech style modeling method for
end-to-end expressive speech synthesis. The proposed method employs
a multi-scale reference encoder to extract both the global-scale utterance-level
and the local-scale quasi-phoneme-level style features of the target
speech, which are then fed into t... | 2104.03521 | title_snapshot |
pan21d_interspeech | Cross-Speaker Style Transfer with Prosody Bottleneck in Neural Speech Synthesis | [
"Shifeng Pan",
"Lei He"
] | https://www.isca-archive.org/interspeech_2021/pan21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/pan21d_interspeech.pdf | 10.21437/Interspeech.2021-979 | 4678-4682 | @inproceedings{pan21d_interspeech,
title = {{Cross-Speaker Style Transfer with Prosody Bottleneck in Neural Speech Synthesis}},
author = {Shifeng Pan and Lei He},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4678--4682},
doi = {10.21437/Interspeech.2021-979},
issn =... | Cross-speaker style transfer is crucial to the applications of multi-style
and expressive speech synthesis at scale. It does not require the target
speakers to be experts in expressing all styles and to collect corresponding
recordings for model training. However, the performances of existing
style transfer methods are... | 2107.12562 | title_snapshot |
tan21_interspeech | Fine-Grained Style Modeling, Transfer and Prediction in Text-to-Speech Synthesis via Phone-Level Content-Style Disentanglement | [
"Daxin Tan",
"Tan Lee"
] | https://www.isca-archive.org/interspeech_2021/tan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/tan21_interspeech.pdf | 10.21437/Interspeech.2021-1129 | 4683-4687 | @inproceedings{tan21_interspeech,
title = {{Fine-Grained Style Modeling, Transfer and Prediction in Text-to-Speech Synthesis via Phone-Level Content-Style Disentanglement}},
author = {Daxin Tan and Tan Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4683--4687},
doi = {... | This paper presents a novel design of neural network system for fine-grained
style modeling, transfer and prediction in expressive text-to-speech
(TTS) synthesis. Fine-grained modeling is realized by extracting style
embeddings from the mel-spectrograms of phone-level speech segments.
Collaborative learning and adversa... | 2011.03943 | title_snapshot |
an21b_interspeech | Improving Performance of Seen and Unseen Speech Style Transfer in End-to-End Neural TTS | [
"Xiaochun An",
"Frank K. Soong",
"Lei Xie"
] | https://www.isca-archive.org/interspeech_2021/an21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/an21b_interspeech.pdf | 10.21437/Interspeech.2021-1407 | 4688-4692 | @inproceedings{an21b_interspeech,
title = {{Improving Performance of Seen and Unseen Speech Style Transfer in End-to-End Neural TTS}},
author = {Xiaochun An and Frank K. Soong and Lei Xie},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4688--4692},
doi = {10.21437/Interspee... | End-to-end neural TTS training has shown improved performance in speech
style transfer. However, the improvement is still limited by the training
data in both target styles and speakers. Inadequate style transfer
performance occurs when the trained TTS tries to transfer the speech
to a target style from a new speaker w... | 2106.10003 | title_snapshot |
shechtman21_interspeech | Synthesis of Expressive Speaking Styles with Limited Training Data in a Multi-Speaker, Prosody-Controllable Sequence-to-Sequence Architecture | [
"Slava Shechtman",
"Raul Fernandez",
"Alexander Sorin",
"David Haws"
] | https://www.isca-archive.org/interspeech_2021/shechtman21_interspeech.html | https://www.isca-archive.org/interspeech_2021/shechtman21_interspeech.pdf | 10.21437/Interspeech.2021-1446 | 4693-4697 | @inproceedings{shechtman21_interspeech,
title = {{Synthesis of Expressive Speaking Styles with Limited Training Data in a Multi-Speaker, Prosody-Controllable Sequence-to-Sequence Architecture}},
author = {Slava Shechtman and Raul Fernandez and Alexander Sorin and David Haws},
year = {2021},
booktitl... | Although Sequence-to-Sequence (S2S) architectures have become state-of-the-art
in speech synthesis, the best models benefit from access to moderate-to-large
amounts of training data, posing a resource bottleneck when we are
interested in generating speech in a variety of expressive styles.
In this work we explore a S2S... | null | null |
dao21_interspeech | Intent Detection and Slot Filling for Vietnamese | [
"Mai Hoang Dao",
"Thinh Hung Truong",
"Dat Quoc Nguyen"
] | https://www.isca-archive.org/interspeech_2021/dao21_interspeech.html | https://www.isca-archive.org/interspeech_2021/dao21_interspeech.pdf | 10.21437/Interspeech.2021-618 | 4698-4702 | @inproceedings{dao21_interspeech,
title = {{Intent Detection and Slot Filling for Vietnamese}},
author = {Mai Hoang Dao and Thinh Hung Truong and Dat Quoc Nguyen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4698--4702},
doi = {10.21437/Interspeech.2021-618},
issn ... | Intent detection and slot filling are important tasks in spoken and
natural language understanding. However, Vietnamese is a low-resource
language in these research topics. In this paper, we present the first public intent detection and slot filling dataset for Vietnamese. In
addition, we also propose a joint model for... | 2104.02021 | title_snapshot |
lin21k_interspeech | Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models | [
"Haitao Lin",
"Lu Xiang",
"Yu Zhou",
"Jiajun Zhang",
"Chengqing Zong"
] | https://www.isca-archive.org/interspeech_2021/lin21k_interspeech.html | https://www.isca-archive.org/interspeech_2021/lin21k_interspeech.pdf | 10.21437/Interspeech.2021-55 | 4703-4707 | @inproceedings{lin21k_interspeech,
title = {{Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models}},
author = {Haitao Lin and Lu Xiang and Yu Zhou and Jiajun Zhang and Chengqing Zong},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4703--4707}... | Spoken Language Understanding (SLU) is one essential step in building
a dialogue system. Due to the expensive cost of obtaining the labeled
data, SLU suffers from the data scarcity problem. Therefore, in this
paper, we focus on data augmentation for slot filling task in SLU.
To achieve that, we aim at generating more d... | 2108.08451 | title_snapshot |
gaspers21_interspeech | The Impact of Intent Distribution Mismatch on Semi-Supervised Spoken Language Understanding | [
"Judith Gaspers",
"Quynh Do",
"Daniil Sorokin",
"Patrick Lehnen"
] | https://www.isca-archive.org/interspeech_2021/gaspers21_interspeech.html | https://www.isca-archive.org/interspeech_2021/gaspers21_interspeech.pdf | 10.21437/Interspeech.2021-335 | 4708-4712 | @inproceedings{gaspers21_interspeech,
title = {{The Impact of Intent Distribution Mismatch on Semi-Supervised Spoken Language Understanding}},
author = {Judith Gaspers and Quynh Do and Daniil Sorokin and Patrick Lehnen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4708--4712},
... | With the expanding role of voice-controlled devices, bootstrapping
spoken language understanding models from little labeled data becomes
essential. Semi-supervised learning is a common technique to improve
model performance when labeled data is scarce. In a real-world production
system, the labeled data and the online ... | null | null |
jiang21c_interspeech | Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification | [
"Yidi Jiang",
"Bidisha Sharma",
"Maulik Madhavi",
"Haizhou Li"
] | https://www.isca-archive.org/interspeech_2021/jiang21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/jiang21c_interspeech.pdf | 10.21437/Interspeech.2021-402 | 4713-4717 | @inproceedings{jiang21c_interspeech,
title = {{Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification}},
author = {Yidi Jiang and Bidisha Sharma and Maulik Madhavi and Haizhou Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4713--4717},
do... | End-to-end intent classification using speech has numerous advantages
compared to the conventional pipeline approach using automatic speech
recognition (ASR), followed by natural language processing modules.
It attempts to predict intent from speech without using an intermediate
ASR module. However, such end-to-end fra... | 2108.02598 | title_snapshot |
wang21ia_interspeech | Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-Trained DNN-HMM-Based Acoustic-Phonetic Model | [
"Nick J.C. Wang",
"Lu Wang",
"Yandan Sun",
"Haimei Kang",
"Dejun Zhang"
] | https://www.isca-archive.org/interspeech_2021/wang21ia_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21ia_interspeech.pdf | 10.21437/Interspeech.2021-501 | 4718-4722 | @inproceedings{wang21ia_interspeech,
title = {{Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-Trained DNN-HMM-Based Acoustic-Phonetic Model}},
author = {Nick J.C. Wang and Lu Wang and Yandan Sun and Haimei Kang and Dejun Zhang},
year = {2021},
booktitle = {{Interspeech ... | In spoken language understanding (SLU), what the user says is converted
to his/her intent. Recent work on end-to-end SLU has shown that accuracy
can be improved via pre-training approaches. We revisit ideas presented
by Lugosch et al. using speech pre-training and three-module modeling;
however, to ease construction of... | 2204.03315 | title_snapshot |
cha21_interspeech | Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputs | [
"Sujeong Cha",
"Wangrui Hou",
"Hyun Jung",
"My Phung",
"Michael Picheny",
"Hong-Kwang J. Kuo",
"Samuel Thomas",
"Edmilson Morais"
] | https://www.isca-archive.org/interspeech_2021/cha21_interspeech.html | https://www.isca-archive.org/interspeech_2021/cha21_interspeech.pdf | 10.21437/Interspeech.2021-788 | 4723-4727 | @inproceedings{cha21_interspeech,
title = {{Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputs}},
author = {Sujeong Cha and Wangrui Hou and Hyun Jung and My Phung and Michael Picheny and Hong-Kwang J. Kuo and Samuel Thomas and Edmilson Morais},
year = {2021},
... | A major focus of recent research in spoken language understanding (SLU)
has been on the end-to-end approach where a single model can predict
intents directly from speech inputs without intermediate transcripts.
However, this approach presents some challenges. First, since speech
can be considered as personally identifi... | 2104.05752 | title_snapshot |
zhang21ha_interspeech | End-to-End Cross-Lingual Spoken Language Understanding Model with Multilingual Pretraining | [
"Xianwei Zhang",
"Liang He"
] | https://www.isca-archive.org/interspeech_2021/zhang21ha_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21ha_interspeech.pdf | 10.21437/Interspeech.2021-818 | 4728-4732 | @inproceedings{zhang21ha_interspeech,
title = {{End-to-End Cross-Lingual Spoken Language Understanding Model with Multilingual Pretraining}},
author = {Xianwei Zhang and Liang He},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4728--4732},
doi = {10.21437/Interspeech.2021-8... | The spoken language understanding (SLU) plays an essential role in
the field of human-computer interaction. Most of the current SLU systems
are cascade systems of automatic speech recognition (ASR) and natural
language understanding (NLU). Error propagation and scarcity of annotated
speech data are two common difficult... | null | null |
saghir21_interspeech | Factorization-Aware Training of Transformers for Natural Language Understanding on the Edge | [
"Hamidreza Saghir",
"Samridhi Choudhary",
"Sepehr Eghbali",
"Clement Chung"
] | https://www.isca-archive.org/interspeech_2021/saghir21_interspeech.html | https://www.isca-archive.org/interspeech_2021/saghir21_interspeech.pdf | 10.21437/Interspeech.2021-1816 | 4733-4737 | @inproceedings{saghir21_interspeech,
title = {{Factorization-Aware Training of Transformers for Natural Language Understanding on the Edge}},
author = {Hamidreza Saghir and Samridhi Choudhary and Sepehr Eghbali and Clement Chung},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4733-... | Fine-tuning transformer-based models have shown to outperform other
methods for many Natural Language Understanding (NLU) tasks. Recent
studies to reduce the size of transformer models have achieved reductions
of > 80%, making on-device inference on powerful devices possible.
However, other resource-constrained devices... | null | null |
saxon21_interspeech | End-to-End Spoken Language Understanding for Generalized Voice Assistants | [
"Michael Saxon",
"Samridhi Choudhary",
"Joseph P. McKenna",
"Athanasios Mouchtaris"
] | https://www.isca-archive.org/interspeech_2021/saxon21_interspeech.html | https://www.isca-archive.org/interspeech_2021/saxon21_interspeech.pdf | 10.21437/Interspeech.2021-1826 | 4738-4742 | @inproceedings{saxon21_interspeech,
title = {{End-to-End Spoken Language Understanding for Generalized Voice Assistants}},
author = {Michael Saxon and Samridhi Choudhary and Joseph P. McKenna and Athanasios Mouchtaris},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4738--4742},
d... | End-to-end (E2E) spoken language understanding (SLU) systems predict
utterance semantics directly from speech using a single model. Previous
work in this area has focused on targeted tasks in fixed domains, where
the output semantic structure is assumed a priori and the input speech
is of limited complexity. In this wo... | 2106.09009 | title_snapshot |
han21f_interspeech | Bi-Directional Joint Neural Networks for Intent Classification and Slot Filling | [
"Soyeon Caren Han",
"Siqu Long",
"Huichun Li",
"Henry Weld",
"Josiah Poon"
] | https://www.isca-archive.org/interspeech_2021/han21f_interspeech.html | https://www.isca-archive.org/interspeech_2021/han21f_interspeech.pdf | 10.21437/Interspeech.2021-2044 | 4743-4747 | @inproceedings{han21f_interspeech,
title = {{Bi-Directional Joint Neural Networks for Intent Classification and Slot Filling}},
author = {Soyeon Caren Han and Siqu Long and Huichun Li and Henry Weld and Josiah Poon},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4743--4747},
doi ... | Intent classification and slot filling are two critical tasks for natural
language understanding. Traditionally the two tasks proceeded independently.
However, more recently joint models for intent classification and slot
filling have achieved state-of-the-art performance, and have proved
that there exists a strong rel... | 2202.13079 | title_snapshot |
cutler21_interspeech | INTERSPEECH 2021 Acoustic Echo Cancellation Challenge | [
"Ross Cutler",
"Ando Saabas",
"Tanel Parnamaa",
"Markus Loide",
"Sten Sootla",
"Marju Purin",
"Hannes Gamper",
"Sebastian Braun",
"Karsten Sorensen",
"Robert Aichner",
"Sriram Srinivasan"
] | https://www.isca-archive.org/interspeech_2021/cutler21_interspeech.html | https://www.isca-archive.org/interspeech_2021/cutler21_interspeech.pdf | 10.21437/Interspeech.2021-1870 | 4748-4752 | @inproceedings{cutler21_interspeech,
title = {{INTERSPEECH 2021 Acoustic Echo Cancellation Challenge}},
author = {Ross Cutler and Ando Saabas and Tanel Parnamaa and Markus Loide and Sten Sootla and Marju Purin and Hannes Gamper and Sebastian Braun and Karsten Sorensen and Robert Aichner and Sriram Srinivasan... | The INTERSPEECH 2021 Acoustic Echo Cancellation Challenge is intended
to stimulate research in the area of acoustic echo cancellation (AEC),
which is an important part of speech enhancement and still a top issue
in audio communication. Many recent AEC studies report good performance
on synthetic datasets where the trai... | null | null |
pfeifenberger21_interspeech | Acoustic Echo Cancellation with Cross-Domain Learning | [
"Lukas Pfeifenberger",
"Matthias Zoehrer",
"Franz Pernkopf"
] | https://www.isca-archive.org/interspeech_2021/pfeifenberger21_interspeech.html | https://www.isca-archive.org/interspeech_2021/pfeifenberger21_interspeech.pdf | 10.21437/Interspeech.2021-85 | 4753-4757 | @inproceedings{pfeifenberger21_interspeech,
title = {{Acoustic Echo Cancellation with Cross-Domain Learning}},
author = {Lukas Pfeifenberger and Matthias Zoehrer and Franz Pernkopf},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4753--4757},
doi = {10.21437/Interspeech.2021... | This paper proposes the Cross-Domain Echo-Controller (CDEC), submitted
to the Interspeech 2021 AEC-Challenge. The algorithm consists of three
building blocks: (i) a Time-Delay Compensation (TDC) module, (ii) a
frequency-domain block-based Acoustic Echo Canceler (AEC), and (iii)
a Time-Domain Neural-Network (TD-NN) used... | null | null |
zhang21ia_interspeech | F-T-LSTM Based Complex Network for Joint Acoustic Echo Cancellation and Speech Enhancement | [
"Shimin Zhang",
"Yuxiang Kong",
"Shubo Lv",
"Yanxin Hu",
"Lei Xie"
] | https://www.isca-archive.org/interspeech_2021/zhang21ia_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21ia_interspeech.pdf | 10.21437/Interspeech.2021-1359 | 4758-4762 | @inproceedings{zhang21ia_interspeech,
title = {{F-T-LSTM Based Complex Network for Joint Acoustic Echo Cancellation and Speech Enhancement}},
author = {Shimin Zhang and Yuxiang Kong and Shubo Lv and Yanxin Hu and Lei Xie},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4758--4762},
... | With the increasing demand for audio communication and online conference,
ensuring the robustness of Acoustic Echo Cancellation (AEC) under the
complicated acoustic scenario including noise, reverberation and nonlinear
distortion has become a top issue. Although there have been some traditional
methods that consider no... | 2106.07577 | title_snapshot |
seidel21_interspeech | Y-Net FCRN for Acoustic Echo and Noise Suppression | [
"Ernst Seidel",
"Jan Franzen",
"Maximilian Strake",
"Tim Fingscheidt"
] | https://www.isca-archive.org/interspeech_2021/seidel21_interspeech.html | https://www.isca-archive.org/interspeech_2021/seidel21_interspeech.pdf | 10.21437/Interspeech.2021-1590 | 4763-4767 | @inproceedings{seidel21_interspeech,
title = {{Y2-Net FCRN for Acoustic Echo and Noise Suppression}},
author = {Ernst Seidel and Jan Franzen and Maximilian Strake and Tim Fingscheidt},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4763--4767},
doi = {10.21437/Interspeech.20... | In recent years, deep neural networks (DNNs) were studied as an alternative
to traditional acoustic echo cancellation (AEC) algorithms. The proposed
models achieved remarkable performance for the separate tasks of AEC
and residual echo suppression (RES). A promising network topology is
a fully convolutional recurrent n... | 2103.17189 | title_judge |
peng21f_interspeech | Acoustic Echo Cancellation Using Deep Complex Neural Network with Nonlinear Magnitude Compression and Phase Information | [
"Renhua Peng",
"Linjuan Cheng",
"Chengshi Zheng",
"Xiaodong Li"
] | https://www.isca-archive.org/interspeech_2021/peng21f_interspeech.html | https://www.isca-archive.org/interspeech_2021/peng21f_interspeech.pdf | 10.21437/Interspeech.2021-2022 | 4768-4772 | @inproceedings{peng21f_interspeech,
title = {{Acoustic Echo Cancellation Using Deep Complex Neural Network with Nonlinear Magnitude Compression and Phase Information}},
author = {Renhua Peng and Linjuan Cheng and Chengshi Zheng and Xiaodong Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pag... | This paper describes a two-stage acoustic echo cancellation (AEC) and
suppression framework for the INTERSPEECH2021 AEC Challenge. In the
first stage, four parallel partitioned block frequency domain adaptive
filters are used to cancel the linear echo components, where the far-end
signal is delayed 0ms, 320ms, 640ms an... | null | null |
ivry21_interspeech | Nonlinear Acoustic Echo Cancellation with Deep Learning | [
"Amir Ivry",
"Israel Cohen",
"Baruch Berdugo"
] | https://www.isca-archive.org/interspeech_2021/ivry21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ivry21_interspeech.pdf | 10.21437/Interspeech.2021-722 | 4773-4777 | @inproceedings{ivry21_interspeech,
title = {{Nonlinear Acoustic Echo Cancellation with Deep Learning}},
author = {Amir Ivry and Israel Cohen and Baruch Berdugo},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4773--4777},
doi = {10.21437/Interspeech.2021-722},
issn = ... | We propose a nonlinear acoustic echo cancellation system, which aims
to model the echo path from the far-end signal to the near-end microphone
in two parts. Inspired by the physical behavior of modern hands-free
devices, we first introduce a novel neural network architecture that
is specifically designed to model the n... | 2106.13754 | title_snapshot |
green21_interspeech | Automatic Speech Recognition of Disordered Speech: Personalized Models Outperforming Human Listeners on Short Phrases | [
"Jordan R. Green",
"Robert L. MacDonald",
"Pan-Pan Jiang",
"Julie Cattiau",
"Rus Heywood",
"Richard Cave",
"Katie Seaver",
"Marilyn A. Ladewig",
"Jimmy Tobin",
"Michael P. Brenner",
"Philip C. Nelson",
"Katrin Tomanek"
] | https://www.isca-archive.org/interspeech_2021/green21_interspeech.html | https://www.isca-archive.org/interspeech_2021/green21_interspeech.pdf | 10.21437/Interspeech.2021-1384 | 4778-4782 | @inproceedings{green21_interspeech,
title = {{Automatic Speech Recognition of Disordered Speech: Personalized Models Outperforming Human Listeners on Short Phrases}},
author = {Jordan R. Green and Robert L. MacDonald and Pan-Pan Jiang and Julie Cattiau and Rus Heywood and Richard Cave and Katie Seaver and Ma... | This study evaluated the accuracy of personalized automatic speech
recognition (ASR) for recognizing disordered speech from a large cohort
of individuals with a wide range of underlying etiologies using an
open vocabulary. The performance of these models was benchmarked relative
to that of expert human transcribers and... | null | null |
neumann21b_interspeech | Investigating the Utility of Multimodal Conversational Technology and Audiovisual Analytic Measures for the Assessment and Monitoring of Amyotrophic Lateral Sclerosis at Scale | [
"Michael Neumann",
"Oliver Roesler",
"Jackson Liscombe",
"Hardik Kothare",
"David Suendermann-Oeft",
"David Pautler",
"Indu Navar",
"Aria Anvar",
"Jochen Kumm",
"Raquel Norel",
"Ernest Fraenkel",
"Alexander V. Sherman",
"James D. Berry",
"Gary L. Pattee",
"Jun Wang",
"Jordan R. Green",... | https://www.isca-archive.org/interspeech_2021/neumann21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/neumann21b_interspeech.pdf | 10.21437/Interspeech.2021-1801 | 4783-4787 | @inproceedings{neumann21b_interspeech,
title = {{Investigating the Utility of Multimodal Conversational Technology and Audiovisual Analytic Measures for the Assessment and Monitoring of Amyotrophic Lateral Sclerosis at Scale}},
author = {Michael Neumann and Oliver Roesler and Jackson Liscombe and Hardik Koth... | We propose a cloud-based multimodal dialog platform for the remote
assessment and monitoring of Amyotrophic Lateral Sclerosis (ALS) at
scale. This paper presents our vision, technology setup, and an initial
investigation of the efficacy of the various acoustic and visual speech
metrics automatically extracted by the pl... | 2104.07310 | title_snapshot |
hermann21_interspeech | Handling Acoustic Variation in Dysarthric Speech Recognition Systems Through Model Combination | [
"Enno Hermann",
"Mathew Magimai-Doss"
] | https://www.isca-archive.org/interspeech_2021/hermann21_interspeech.html | https://www.isca-archive.org/interspeech_2021/hermann21_interspeech.pdf | 10.21437/Interspeech.2021-2212 | 4788-4792 | @inproceedings{hermann21_interspeech,
title = {{Handling Acoustic Variation in Dysarthric Speech Recognition Systems Through Model Combination}},
author = {Enno Hermann and Mathew Magimai-Doss},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4788--4792},
doi = {10.21437/Inte... | Developing automatic speech recognition (ASR) systems that recognise
dysarthric speech as well as control speech from unimpaired speakers
remains challenging. Including more highly variable dysarthric speech
during training can also negatively affect the performance on control
speakers, which is not desirable when deve... | null | null |
geng21b_interspeech | Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition | [
"Mengzhe Geng",
"Shansong Liu",
"Jianwei Yu",
"Xurong Xie",
"Shoukang Hu",
"Zi Ye",
"Zengrui Jin",
"Xunying Liu",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2021/geng21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/geng21b_interspeech.pdf | 10.21437/Interspeech.2021-60 | 4793-4797 | @inproceedings{geng21b_interspeech,
title = {{Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition}},
author = {Mengzhe Geng and Shansong Liu and Jianwei Yu and Xurong Xie and Shoukang Hu and Zi Ye and Zengrui Jin and Xunying Liu and Helen Meng},
year = {2021},
booktitle =... | Automatic recognition of disordered speech remains a highly challenging
task to date. Sources of variability commonly found in normal speech
including accent, age or gender, when further compounded with the underlying
causes of speech impairment and varying severity levels, create large
diversity among speakers. To thi... | 2201.05554 | title_snapshot |
gutz21_interspeech | Speaking with a KN95 Face Mask: ASR Performance and Speaker Compensation | [
"Sarah E. Gutz",
"Hannah P. Rowe",
"Jordan R. Green"
] | https://www.isca-archive.org/interspeech_2021/gutz21_interspeech.html | https://www.isca-archive.org/interspeech_2021/gutz21_interspeech.pdf | 10.21437/Interspeech.2021-99 | 4798-4802 | @inproceedings{gutz21_interspeech,
title = {{Speaking with a KN95 Face Mask: ASR Performance and Speaker Compensation}},
author = {Sarah E. Gutz and Hannah P. Rowe and Jordan R. Green},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4798--4802},
doi = {10.21437/Interspeech.2... | The increasing prevalence of face masks in the United States due to
the COVID-19 pandemic necessitates serious consideration of the functional
impact of wearing a mask on speech. This study considers how the presence
of a KN95 mask affects the performance of a commercial ASR system,
Google Cloud Speech. We present evid... | null | null |
jin21_interspeech | Adversarial Data Augmentation for Disordered Speech Recognition | [
"Zengrui Jin",
"Mengzhe Geng",
"Xurong Xie",
"Jianwei Yu",
"Shansong Liu",
"Xunying Liu",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2021/jin21_interspeech.html | https://www.isca-archive.org/interspeech_2021/jin21_interspeech.pdf | 10.21437/Interspeech.2021-168 | 4803-4807 | @inproceedings{jin21_interspeech,
title = {{Adversarial Data Augmentation for Disordered Speech Recognition}},
author = {Zengrui Jin and Mengzhe Geng and Xurong Xie and Jianwei Yu and Shansong Liu and Xunying Liu and Helen Meng},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4803--... | Automatic recognition of disordered speech remains a highly challenging
task to date. The underlying neuro-motor conditions, often compounded
with co-occurring physical disabilities, lead to the difficulty in
collecting large quantities of impaired speech required for ASR system
development. To this end, data augmentat... | 2108.00899 | title_snapshot |
xie21b_interspeech | Variational Auto-Encoder Based Variability Encoding for Dysarthric Speech Recognition | [
"Xurong Xie",
"Rukiye Ruzi",
"Xunying Liu",
"Lan Wang"
] | https://www.isca-archive.org/interspeech_2021/xie21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/xie21b_interspeech.pdf | 10.21437/Interspeech.2021-173 | 4808-4812 | @inproceedings{xie21b_interspeech,
title = {{Variational Auto-Encoder Based Variability Encoding for Dysarthric Speech Recognition}},
author = {Xurong Xie and Rukiye Ruzi and Xunying Liu and Lan Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4808--4812},
doi = {10.214... | Dysarthric speech recognition is a challenging task due to acoustic
variability and limited amount of available data. Diverse conditions
of dysarthric speakers account for the acoustic variability, which
make the variability difficult to be modeled precisely. This paper
presents a variational auto-encoder based variabi... | 2201.09422 | title_snapshot |
wang21ja_interspeech | Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion | [
"Disong Wang",
"Songxiang Liu",
"Lifa Sun",
"Xixin Wu",
"Xunying Liu",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2021/wang21ja_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21ja_interspeech.pdf | 10.21437/Interspeech.2021-285 | 4813-4817 | @inproceedings{wang21ja_interspeech,
title = {{Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion}},
author = {Disong Wang and Songxiang Liu and Lifa Sun and Xixin Wu and Xunying Liu and Helen Meng},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | Though significant progress has been made for the voice conversion
(VC) of typical speech, VC for atypical speech, e.g., dysarthric and
second-language (L2) speech, remains a challenge, since it involves
correcting for atypical prosody while maintaining speaker identity.
To address this issue, we propose a VC system wi... | 2011.01678 | title_snapshot |
deng21d_interspeech | Bayesian Parametric and Architectural Domain Adaptation of LF-MMI Trained TDNNs for Elderly and Dysarthric Speech Recognition | [
"Jiajun Deng",
"Fabian Ritter Gutierrez",
"Shoukang Hu",
"Mengzhe Geng",
"Xurong Xie",
"Zi Ye",
"Shansong Liu",
"Jianwei Yu",
"Xunying Liu",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2021/deng21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/deng21d_interspeech.pdf | 10.21437/Interspeech.2021-289 | 4818-4822 | @inproceedings{deng21d_interspeech,
title = {{Bayesian Parametric and Architectural Domain Adaptation of LF-MMI Trained TDNNs for Elderly and Dysarthric Speech Recognition}},
author = {Jiajun Deng and Fabian Ritter Gutierrez and Shoukang Hu and Mengzhe Geng and Xurong Xie and Zi Ye and Shansong Liu and Jianw... | Automatic recognition of elderly and disordered speech remains a highly
challenging task to date. Such data is not only difficult to collect
in large quantities, but also exhibits a significant mismatch against
normal speech trained ASR systems. To this end, conventional deep neural
network model adaptation approaches ... | null | null |
cai21c_interspeech | A Voice-Activated Switch for Persons with Motor and Speech Impairments: Isolated-Vowel Spotting Using Neural Networks | [
"Shanqing Cai",
"Lisie Lillianfeld",
"Katie Seaver",
"Jordan R. Green",
"Michael P. Brenner",
"Philip C. Nelson",
"D. Sculley"
] | https://www.isca-archive.org/interspeech_2021/cai21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/cai21c_interspeech.pdf | 10.21437/Interspeech.2021-330 | 4823-4827 | @inproceedings{cai21c_interspeech,
title = {{A Voice-Activated Switch for Persons with Motor and Speech Impairments: Isolated-Vowel Spotting Using Neural Networks}},
author = {Shanqing Cai and Lisie Lillianfeld and Katie Seaver and Jordan R. Green and Michael P. Brenner and Philip C. Nelson and D. Sculley},
... | Severe speech impairments limit the precision and range of producible
speech sounds. As a result, generic automatic speech recognition (ASR)
and keyword spotting (KWS) systems fail to accurately recognize the
utterances produced by individuals with severe speech impairments.
This paper describes an approach in a simple... | null | null |
chen21w_interspeech | Conformer Parrotron: A Faster and Stronger End-to-End Speech Conversion and Recognition Model for Atypical Speech | [
"Zhehuai Chen",
"Bhuvana Ramabhadran",
"Fadi Biadsy",
"Xia Zhang",
"Youzheng Chen",
"Liyang Jiang",
"Fang Chu",
"Rohan Doshi",
"Pedro J. Moreno"
] | https://www.isca-archive.org/interspeech_2021/chen21w_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21w_interspeech.pdf | 10.21437/Interspeech.2021-676 | 4828-4832 | @inproceedings{chen21w_interspeech,
title = {{Conformer Parrotron: A Faster and Stronger End-to-End Speech Conversion and Recognition Model for Atypical Speech}},
author = {Zhehuai Chen and Bhuvana Ramabhadran and Fadi Biadsy and Xia Zhang and Youzheng Chen and Liyang Jiang and Fang Chu and Rohan Doshi and P... | Parrotron is an end-to-end personalizable model that enables many-to-one
voice conversion (VC) and automated speech recognition (ASR) simultaneously
for atypical speech. In this work, we present the next-generation Parrotron
model with improvements in overall accuracy, training and inference
speeds. The proposed archit... | null | null |
macdonald21_interspeech | Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia | [
"Robert L. MacDonald",
"Pan-Pan Jiang",
"Julie Cattiau",
"Rus Heywood",
"Richard Cave",
"Katie Seaver",
"Marilyn A. Ladewig",
"Jimmy Tobin",
"Michael P. Brenner",
"Philip C. Nelson",
"Jordan R. Green",
"Katrin Tomanek"
] | https://www.isca-archive.org/interspeech_2021/macdonald21_interspeech.html | https://www.isca-archive.org/interspeech_2021/macdonald21_interspeech.pdf | 10.21437/Interspeech.2021-697 | 4833-4837 | @inproceedings{macdonald21_interspeech,
title = {{Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia}},
author = {Robert L. MacDonald and Pan-Pan Jiang and Julie Cattiau and Rus Heywood and Richard Cave and Katie Seaver and Marilyn A. Ladewig and Jimmy Tobin and ... | Speech samples from over 1000 individuals with impaired speech have
been submitted for Project Euphonia, aimed at improving automated speech
recognition systems for disordered speech. We provide an overview of
the corpus, which recently passed 1 million utterances (>1300 hours),
and review key lessons learned from this... | null | null |
yeo21_interspeech | Automatic Severity Classification of Korean Dysarthric Speech Using Phoneme-Level Pronunciation Features | [
"Eun Jung Yeo",
"Sunhee Kim",
"Minhwa Chung"
] | https://www.isca-archive.org/interspeech_2021/yeo21_interspeech.html | https://www.isca-archive.org/interspeech_2021/yeo21_interspeech.pdf | 10.21437/Interspeech.2021-1353 | 4838-4842 | @inproceedings{yeo21_interspeech,
title = {{Automatic Severity Classification of Korean Dysarthric Speech Using Phoneme-Level Pronunciation Features}},
author = {Eun Jung Yeo and Sunhee Kim and Minhwa Chung},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4838--4842},
doi = ... | This paper proposes an automatic severity classification method for
Korean dysarthric speech by using two types of phoneme-level pronunciation
features. The first type is the percentage of correct phonemes, which
consists of percentage of correct consonants, percentage of correct
vowels, and percentage of total correct... | null | null |
venugopalan21_interspeech | Comparing Supervised Models and Learned Speech Representations for Classifying Intelligibility of Disordered Speech on Selected Phrases | [
"Subhashini Venugopalan",
"Joel Shor",
"Manoj Plakal",
"Jimmy Tobin",
"Katrin Tomanek",
"Jordan R. Green",
"Michael P. Brenner"
] | https://www.isca-archive.org/interspeech_2021/venugopalan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/venugopalan21_interspeech.pdf | 10.21437/Interspeech.2021-1913 | 4843-4847 | @inproceedings{venugopalan21_interspeech,
title = {{Comparing Supervised Models and Learned Speech Representations for Classifying Intelligibility of Disordered Speech on Selected Phrases}},
author = {Subhashini Venugopalan and Joel Shor and Manoj Plakal and Jimmy Tobin and Katrin Tomanek and Jordan R. Green... | Automatic classification of disordered speech can provide an objective
tool for identifying the presence and severity of a speech impairment.
Classification approaches can also help identify hard-to-recognize
speech samples to teach ASR systems about the variable manifestations
of impaired speech. Here, we develop and ... | 2107.03985 | title_snapshot |
mitra21_interspeech | Analysis and Tuning of a Voice Assistant System for Dysfluent Speech | [
"Vikramjit Mitra",
"Zifang Huang",
"Colin Lea",
"Lauren Tooley",
"Sarah Wu",
"Darren Botten",
"Ashwini Palekar",
"Shrinath Thelapurath",
"Panayiotis Georgiou",
"Sachin Kajarekar",
"Jefferey Bigham"
] | https://www.isca-archive.org/interspeech_2021/mitra21_interspeech.html | https://www.isca-archive.org/interspeech_2021/mitra21_interspeech.pdf | 10.21437/Interspeech.2021-2006 | 4848-4852 | @inproceedings{mitra21_interspeech,
title = {{Analysis and Tuning of a Voice Assistant System for Dysfluent Speech}},
author = {Vikramjit Mitra and Zifang Huang and Colin Lea and Lauren Tooley and Sarah Wu and Darren Botten and Ashwini Palekar and Shrinath Thelapurath and Panayiotis Georgiou and Sachin Kajar... | Dysfluencies and variations in speech pronunciation can severely degrade
speech recognition performance, and for many individuals with moderate-to-severe
speech disorders, voice operated systems do not work. Current speech
recognition systems are trained primarily with data from fluent speakers
and as a consequence do ... | 2106.11759 | title_snapshot |
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