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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|---|---|---|---|---|---|---|---|---|---|---|
allauzen21_interspeech | A Hybrid Seq-2-Seq ASR Design for On-Device and Server Applications | [
"Cyril Allauzen",
"Ehsan Variani",
"Michael Riley",
"David Rybach",
"Hao Zhang"
] | https://www.isca-archive.org/interspeech_2021/allauzen21_interspeech.html | https://www.isca-archive.org/interspeech_2021/allauzen21_interspeech.pdf | 10.21437/Interspeech.2021-658 | 4044-4048 | @inproceedings{allauzen21_interspeech,
title = {{A Hybrid Seq-2-Seq ASR Design for On-Device and Server Applications}},
author = {Cyril Allauzen and Ehsan Variani and Michael Riley and David Rybach and Hao Zhang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4044--4048},
doi ... | This paper proposes and evaluates alternative speech recognition design
strategies using the hybrid autoregressive transducer (HAT) model.
The different strategies are designed with special attention to the
choice of modeling units and to the integration of different types
of external language models during first-pass ... | null | null |
inaguma21b_interspeech | VAD-Free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording | [
"Hirofumi Inaguma",
"Tatsuya Kawahara"
] | https://www.isca-archive.org/interspeech_2021/inaguma21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/inaguma21b_interspeech.pdf | 10.21437/Interspeech.2021-1107 | 4049-4053 | @inproceedings{inaguma21b_interspeech,
title = {{VAD-Free Streaming Hybrid CTC/Attention ASR for Unsegmented Recording}},
author = {Hirofumi Inaguma and Tatsuya Kawahara},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4049--4053},
doi = {10.21437/Interspeech.2021-1107},
i... | In this work, we propose novel decoding algorithms to enable streaming
automatic speech recognition (ASR) on unsegmented long-form recordings
without voice activity detection (VAD), based on monotonic chunkwise
attention (MoChA) with an auxiliary connectionist temporal classification
(CTC) objective. We propose a block... | 2107.07509 | title_snapshot |
yao21_interspeech | WeNet: Production Oriented Streaming and Non-Streaming End-to-End Speech Recognition Toolkit | [
"Zhuoyuan Yao",
"Di Wu",
"Xiong Wang",
"Binbin Zhang",
"Fan Yu",
"Chao Yang",
"Zhendong Peng",
"Xiaoyu Chen",
"Lei Xie",
"Xin Lei"
] | https://www.isca-archive.org/interspeech_2021/yao21_interspeech.html | https://www.isca-archive.org/interspeech_2021/yao21_interspeech.pdf | 10.21437/Interspeech.2021-1983 | 4054-4058 | @inproceedings{yao21_interspeech,
title = {{WeNet: Production Oriented Streaming and Non-Streaming End-to-End Speech Recognition Toolkit}},
author = {Zhuoyuan Yao and Di Wu and Xiong Wang and Binbin Zhang and Fan Yu and Chao Yang and Zhendong Peng and Xiaoyu Chen and Lei Xie and Xin Lei},
year = {2021... | In this paper, we propose an open source speech recognition toolkit
called WeNet, in which a new two-pass approach named U2 is implemented
to unify streaming and non-streaming end-to-end (E2E) speech recognition
in a single model. The main motivation of WeNet is to close the gap
between the research and deployment of E... | 2102.01547 | title_snapshot |
tanaka21b_interspeech | Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition | [
"Tomohiro Tanaka",
"Ryo Masumura",
"Mana Ihori",
"Akihiko Takashima",
"Takafumi Moriya",
"Takanori Ashihara",
"Shota Orihashi",
"Naoki Makishima"
] | https://www.isca-archive.org/interspeech_2021/tanaka21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/tanaka21b_interspeech.pdf | 10.21437/Interspeech.2021-1992 | 4059-4063 | @inproceedings{tanaka21b_interspeech,
title = {{Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition}},
author = {Tomohiro Tanaka and Ryo Masumura and Mana Ihori and Akihiko Takashima and Takafumi Moriya and Takanori Ashihara and Shota Orihashi and Naoki Makishima},
year ... | We propose a cross-modal transformer-based neural correction models
that refines the output of an automatic speech recognition (ASR) system
so as to exclude ASR errors. Generally, neural correction models are
composed of encoder-decoder networks, which can directly model sequence-to-sequence
mapping problems. The most ... | 2107.01569 | title_snapshot |
lee21f_interspeech | Deep Neural Network Calibration for E2E Speech Recognition System | [
"Mun-Hak Lee",
"Joon-Hyuk Chang"
] | https://www.isca-archive.org/interspeech_2021/lee21f_interspeech.html | https://www.isca-archive.org/interspeech_2021/lee21f_interspeech.pdf | 10.21437/Interspeech.2021-176 | 4064-4068 | @inproceedings{lee21f_interspeech,
title = {{Deep Neural Network Calibration for E2E Speech Recognition System}},
author = {Mun-Hak Lee and Joon-Hyuk Chang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4064--4068},
doi = {10.21437/Interspeech.2021-176},
issn = {295... | Cross-entropy loss, which is commonly used in deep-neural-network-based
(DNN) classification model training, induces models to assign a high
probability value to one class. Networks trained in this fashion tend
to be overconfident, which causes a problem in the decoding process
of the speech recognition system, as it u... | null | null |
li21m_interspeech | Residual Energy-Based Models for End-to-End Speech Recognition | [
"Qiujia Li",
"Yu Zhang",
"Bo Li",
"Liangliang Cao",
"Philip C. Woodland"
] | https://www.isca-archive.org/interspeech_2021/li21m_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21m_interspeech.pdf | 10.21437/Interspeech.2021-690 | 4069-4073 | @inproceedings{li21m_interspeech,
title = {{Residual Energy-Based Models for End-to-End Speech Recognition}},
author = {Qiujia Li and Yu Zhang and Bo Li and Liangliang Cao and Philip C. Woodland},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4069--4073},
doi = {10.21437/In... | End-to-end models with auto-regressive decoders have shown impressive
results for automatic speech recognition (ASR). These models formulate
the sequence-level probability as a product of the conditional probabilities
of all individual tokens given their histories. However, the performance
of locally normalised models ... | 2103.14152 | title_snapshot |
qiu21b_interspeech | Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction | [
"David Qiu",
"Yanzhang He",
"Qiujia Li",
"Yu Zhang",
"Liangliang Cao",
"Ian McGraw"
] | https://www.isca-archive.org/interspeech_2021/qiu21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/qiu21b_interspeech.pdf | 10.21437/Interspeech.2021-1207 | 4074-4078 | @inproceedings{qiu21b_interspeech,
title = {{Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction}},
author = {David Qiu and Yanzhang He and Qiujia Li and Yu Zhang and Liangliang Cao and Ian McGraw},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | Confidence scores are very useful for downstream applications of automatic
speech recognition (ASR) systems. Recent works have proposed using
neural networks to learn word or utterance confidence scores for end-to-end
ASR. In those studies, word confidence by itself does not model deletions,
and utterance confidence do... | 2104.12870 | title_snapshot |
ollerenshaw21_interspeech | Insights on Neural Representations for End-to-End Speech Recognition | [
"Anna Ollerenshaw",
"Md. Asif Jalal",
"Thomas Hain"
] | https://www.isca-archive.org/interspeech_2021/ollerenshaw21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ollerenshaw21_interspeech.pdf | 10.21437/Interspeech.2021-1516 | 4079-4083 | @inproceedings{ollerenshaw21_interspeech,
title = {{Insights on Neural Representations for End-to-End Speech Recognition}},
author = {Anna Ollerenshaw and Md. Asif Jalal and Thomas Hain},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4079--4083},
doi = {10.21437/Interspeech... | End-to-end automatic speech recognition (ASR) models aim to learn a
generalised speech representation. However, there are limited tools
available to understand the internal functions and the effect of hierarchical
dependencies within the model architecture. It is crucial to understand
the correlations between the layer... | 2205.09456 | title_snapshot |
afshan21_interspeech | Sequence-Level Confidence Classifier for ASR Utterance Accuracy and Application to Acoustic Models | [
"Amber Afshan",
"Kshitiz Kumar",
"Jian Wu"
] | https://www.isca-archive.org/interspeech_2021/afshan21_interspeech.html | https://www.isca-archive.org/interspeech_2021/afshan21_interspeech.pdf | 10.21437/Interspeech.2021-1666 | 4084-4088 | @inproceedings{afshan21_interspeech,
title = {{Sequence-Level Confidence Classifier for ASR Utterance Accuracy and Application to Acoustic Models}},
author = {Amber Afshan and Kshitiz Kumar and Jian Wu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4084--4088},
doi = {10.2... | Scores from traditional confidence classifiers (CCs) in automatic speech
recognition (ASR) systems lack universal interpretation and vary with
updates to the underlying confidence or acoustic models (AMs). In this
work, we build interpretable confidence scores with an objective to
closely align with ASR accuracy. We pr... | 2107.00099 | title_snapshot |
tjandra21_interspeech | Unsupervised Learning of Disentangled Speech Content and Style Representation | [
"Andros Tjandra",
"Ruoming Pang",
"Yu Zhang",
"Shigeki Karita"
] | https://www.isca-archive.org/interspeech_2021/tjandra21_interspeech.html | https://www.isca-archive.org/interspeech_2021/tjandra21_interspeech.pdf | 10.21437/Interspeech.2021-1936 | 4089-4093 | @inproceedings{tjandra21_interspeech,
title = {{Unsupervised Learning of Disentangled Speech Content and Style Representation}},
author = {Andros Tjandra and Ruoming Pang and Yu Zhang and Shigeki Karita},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4089--4093},
doi = {10.... | Speech is influenced by a number of underlying factors, which can be
broadly categorized into linguistic contents and speaking styles. However,
collecting the labeled data that annotates both content and style is
an expensive and time-consuming task. Here, we present an approach
for unsupervised learning of speech repr... | 2010.12973 | title_snapshot |
choi21_interspeech | Label Embedding for Chinese Grapheme-to-Phoneme Conversion | [
"Eunbi Choi",
"Hwa-Yeon Kim",
"Jong-Hwan Kim",
"Jae-Min Kim"
] | https://www.isca-archive.org/interspeech_2021/choi21_interspeech.html | https://www.isca-archive.org/interspeech_2021/choi21_interspeech.pdf | 10.21437/Interspeech.2021-885 | 4094-4098 | @inproceedings{choi21_interspeech,
title = {{Label Embedding for Chinese Grapheme-to-Phoneme Conversion}},
author = {Eunbi Choi and Hwa-Yeon Kim and Jong-Hwan Kim and Jae-Min Kim},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4094--4098},
doi = {10.21437/Interspeech.2021-8... | Chinese grapheme-to-phoneme (G2P) conversion plays a significant role
in text-to-speech systems by generating pronunciations corresponding
to Chinese input characters. The main challenge in Chinese G2P conversion
is polyphone disambiguation, which requires selecting the appropriate
pronunciation among several candidate... | null | null |
zhang21aa_interspeech | PDF: Polyphone Disambiguation in Chinese by Using FLAT | [
"Haiteng Zhang"
] | https://www.isca-archive.org/interspeech_2021/zhang21aa_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21aa_interspeech.pdf | 10.21437/Interspeech.2021-1087 | 4099-4103 | @inproceedings{zhang21aa_interspeech,
title = {{PDF: Polyphone Disambiguation in Chinese by Using FLAT}},
author = {Haiteng Zhang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4099--4103},
doi = {10.21437/Interspeech.2021-1087},
issn = {2958-1796},
} | Polyphone disambiguation is an essential procedure in the front-end
module of the Chinese text-to-speech (TTS) system. It serves to predict
the pronunciation of the input polyphonic character. In the Chinese
TTS system, a well-designed pronunciation dictionary plays a crucial
role in supplying pinyin to words. However,... | null | null |
li21n_interspeech | Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-Pooling Strategy and Window-Based Attention | [
"Junjie Li",
"Zhiyu Zhang",
"Minchuan Chen",
"Jun Ma",
"Shaojun Wang",
"Jing Xiao"
] | https://www.isca-archive.org/interspeech_2021/li21n_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21n_interspeech.pdf | 10.21437/Interspeech.2021-1232 | 4104-4108 | @inproceedings{li21n_interspeech,
title = {{Improving Polyphone Disambiguation for Mandarin Chinese by Combining Mix-Pooling Strategy and Window-Based Attention}},
author = {Junjie Li and Zhiyu Zhang and Minchuan Chen and Jun Ma and Shaojun Wang and Jing Xiao},
year = {2021},
booktitle = {{Interspee... | In this paper, we propose a novel system based on word-level features
and window-based attention for polyphone disambiguation, which is a
fundamental task for Grapheme-to-phoneme (G2P) conversion of Mandarin
Chinese. The framework aims to combine a pre-trained language model
with explicit word-level information in orde... | null | null |
shi21d_interspeech | Polyphone Disambiguation in Mandarin Chinese with Semi-Supervised Learning | [
"Yi Shi",
"Congyi Wang",
"Yu Chen",
"Bin Wang"
] | https://www.isca-archive.org/interspeech_2021/shi21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/shi21d_interspeech.pdf | 10.21437/Interspeech.2021-502 | 4109-4113 | @inproceedings{shi21d_interspeech,
title = {{Polyphone Disambiguation in Mandarin Chinese with Semi-Supervised Learning}},
author = {Yi Shi and Congyi Wang and Yu Chen and Bin Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4109--4113},
doi = {10.21437/Interspeech.2021... | The majority of Chinese characters are monophonic, while a special
group of characters, called polyphonic characters, have multiple pronunciations.
As a prerequisite of performing speech-related generative tasks, the
correct pronunciation must be identified among several candidates.
This process is called Polyphone Dis... | 2102.00621 | title_snapshot |
chen21s_interspeech | A Neural-Network-Based Approach to Identifying Speakers in Novels | [
"Yue Chen",
"Zhen-Hua Ling",
"Qing-Feng Liu"
] | https://www.isca-archive.org/interspeech_2021/chen21s_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21s_interspeech.pdf | 10.21437/Interspeech.2021-609 | 4114-4118 | @inproceedings{chen21s_interspeech,
title = {{A Neural-Network-Based Approach to Identifying Speakers in Novels}},
author = {Yue Chen and Zhen-Hua Ling and Qing-Feng Liu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4114--4118},
doi = {10.21437/Interspeech.2021-609},
is... | Identifying speakers in novels aims at determining who says a quote
in a given context by text analysis. This task is important for speech
synthesis systems to assign appropriate voices to the quotes when producing
audiobooks. However, existing approaches stick with manual features
and traditional machine learning clas... | null | null |
zhou21f_interspeech | UnitNet-Based Hybrid Speech Synthesis | [
"Xiao Zhou",
"Zhen-Hua Ling",
"Li-Rong Dai"
] | https://www.isca-archive.org/interspeech_2021/zhou21f_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhou21f_interspeech.pdf | 10.21437/Interspeech.2021-1092 | 4119-4123 | @inproceedings{zhou21f_interspeech,
title = {{UnitNet-Based Hybrid Speech Synthesis}},
author = {Xiao Zhou and Zhen-Hua Ling and Li-Rong Dai},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4119--4123},
doi = {10.21437/Interspeech.2021-1092},
issn = {2958-1796},
} | This paper presents a hybrid speech synthesis method based on UnitNet,
a unified sequence-to-sequence (Seq2Seq) acoustic model for both statistical
parametric speech synthesis (SPSS) and concatenative speech synthesis
(CSS). This method combines CSS and SPSS approaches to synthesize different
segments in an utterance. ... | null | null |
novitasari21_interspeech | Dynamically Adaptive Machine Speech Chain Inference for TTS in Noisy Environment: Listen and Speak Louder | [
"Sashi Novitasari",
"Sakriani Sakti",
"Satoshi Nakamura"
] | https://www.isca-archive.org/interspeech_2021/novitasari21_interspeech.html | https://www.isca-archive.org/interspeech_2021/novitasari21_interspeech.pdf | 10.21437/Interspeech.2021-946 | 4124-4128 | @inproceedings{novitasari21_interspeech,
title = {{Dynamically Adaptive Machine Speech Chain Inference for TTS in Noisy Environment: Listen and Speak Louder}},
author = {Sashi Novitasari and Sakriani Sakti and Satoshi Nakamura},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4124--4... | Although machine speech chains were originally proposed to mimic a
closed-loop human speech chain mechanism with auditory feedback, the
existing machine speech chains are only utilized as a semi-supervised
learning method that allows automatic speech recognition (ASR) and
text-to-speech synthesis systems (TTS) to suppo... | null | null |
zhang21ba_interspeech | LinearSpeech: Parallel Text-to-Speech with Linear Complexity | [
"Haozhe Zhang",
"Zhihua Huang",
"Zengqiang Shang",
"Pengyuan Zhang",
"Yonghong Yan"
] | https://www.isca-archive.org/interspeech_2021/zhang21ba_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21ba_interspeech.pdf | 10.21437/Interspeech.2021-1192 | 4129-4133 | @inproceedings{zhang21ba_interspeech,
title = {{LinearSpeech: Parallel Text-to-Speech with Linear Complexity}},
author = {Haozhe Zhang and Zhihua Huang and Zengqiang Shang and Pengyuan Zhang and Yonghong Yan},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4129--4133},
doi =... | Non-autoregressive text to speech models such as FastSpeech can synthesize
speech significantly faster than previous autoregressive models with
comparable quality. However, the memory and time complexity O(N 2 ) of self-attention hinders FastSpeech from generating long sequences,
where N is the length of mel-spectrogra... | null | null |
mansbach21_interspeech | An Agent for Competing with Humans in a Deceptive Game Based on Vocal Cues | [
"Noa Mansbach",
"Evgeny Hershkovitch Neiterman",
"Amos Azaria"
] | https://www.isca-archive.org/interspeech_2021/mansbach21_interspeech.html | https://www.isca-archive.org/interspeech_2021/mansbach21_interspeech.pdf | 10.21437/Interspeech.2021-83 | 4134-4138 | @inproceedings{mansbach21_interspeech,
title = {{An Agent for Competing with Humans in a Deceptive Game Based on Vocal Cues}},
author = {Noa Mansbach and Evgeny Hershkovitch Neiterman and Amos Azaria},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4134--4138},
doi = {10.214... | In this work we present the development of an autonomous agent capable
of competing with humans in a deception-based game. The agent predicts
whether a given statement is true or false based on vocal cues. To
this end, we develop a game for collecting a large scale and high quality
labeled sound data-set in a controlle... | null | null |
fakhry21_interspeech | A Multi-Branch Deep Learning Network for Automated Detection of COVID-19 | [
"Ahmed Fakhry",
"Xinyi Jiang",
"Jaclyn Xiao",
"Gunvant Chaudhari",
"Asriel Han"
] | https://www.isca-archive.org/interspeech_2021/fakhry21_interspeech.html | https://www.isca-archive.org/interspeech_2021/fakhry21_interspeech.pdf | 10.21437/Interspeech.2021-378 | 4139-4143 | @inproceedings{fakhry21_interspeech,
title = {{A Multi-Branch Deep Learning Network for Automated Detection of COVID-19}},
author = {Ahmed Fakhry and Xinyi Jiang and Jaclyn Xiao and Gunvant Chaudhari and Asriel Han},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4139--4143},
doi ... | Fast and affordable solutions for COVID-19 testing are necessary to
contain the spread of the global pandemic and help relieve the burden
on medical facilities. Currently, limited testing locations and expensive
equipment pose difficulties for individuals seeking testing, especially
in low-resource settings. Researcher... | null | null |
ma21d_interspeech | RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform | [
"Youxuan Ma",
"Zongze Ren",
"Shugong Xu"
] | https://www.isca-archive.org/interspeech_2021/ma21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/ma21d_interspeech.pdf | 10.21437/Interspeech.2021-438 | 4144-4148 | @inproceedings{ma21d_interspeech,
title = {{RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform}},
author = {Youxuan Ma and Zongze Ren and Shugong Xu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4144--4148},
doi = {10.21437/Interspeech.2021-438},
issn ... | In recent years, synthetic speech generated by advanced text-to-speech
(TTS) and voice conversion (VC) systems has caused great harms to automatic
speaker verification (ASV) systems, urging us to design a synthetic
speech detection system to protect ASV systems. In this paper, we propose
a new speech anti-spoofing mode... | 2108.05684 | title_snapshot |
dhamyal21_interspeech | Fake Audio Detection in Resource-Constrained Settings Using Microfeatures | [
"Hira Dhamyal",
"Ayesha Ali",
"Ihsan Ayyub Qazi",
"Agha Ali Raza"
] | https://www.isca-archive.org/interspeech_2021/dhamyal21_interspeech.html | https://www.isca-archive.org/interspeech_2021/dhamyal21_interspeech.pdf | 10.21437/Interspeech.2021-524 | 4149-4153 | @inproceedings{dhamyal21_interspeech,
title = {{Fake Audio Detection in Resource-Constrained Settings Using Microfeatures}},
author = {Hira Dhamyal and Ayesha Ali and Ihsan Ayyub Qazi and Agha Ali Raza},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4149--4153},
doi = {10.2... | Fake audio generation has undergone remarkable improvement with the
advancement in deep neural network models. This has made it increasingly
important to develop lightweight yet robust mechanisms for detecting
fake audios, especially for resource-constrained settings such as on
edge devices and embedded controllers as ... | null | null |
yan21c_interspeech | Coughing-Based Recognition of Covid-19 with Spatial Attentive ConvLSTM Recurrent Neural Networks | [
"Tianhao Yan",
"Hao Meng",
"Emilia Parada-Cabaleiro",
"Shuo Liu",
"Meishu Song",
"Björn W. Schuller"
] | https://www.isca-archive.org/interspeech_2021/yan21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/yan21c_interspeech.pdf | 10.21437/Interspeech.2021-630 | 4154-4158 | @inproceedings{yan21c_interspeech,
title = {{Coughing-Based Recognition of Covid-19 with Spatial Attentive ConvLSTM Recurrent Neural Networks}},
author = {Tianhao Yan and Hao Meng and Emilia Parada-Cabaleiro and Shuo Liu and Meishu Song and Björn W. Schuller},
year = {2021},
booktitle = {{Interspeec... | The rapid emergence of COVID-19 has become a major public health threat
around the world. Although early detection is crucial to reduce its
spread, the existing diagnostic methods are still insufficient in bringing
the pandemic under control. Thus, more sophisticated systems, able
to easily identify the infection from ... | null | null |
paul21b_interspeech | Knowledge Distillation for Singing Voice Detection | [
"Soumava Paul",
"Gurunath Reddy M",
"K. Sreenivasa Rao",
"Partha Pratim Das"
] | https://www.isca-archive.org/interspeech_2021/paul21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/paul21b_interspeech.pdf | 10.21437/Interspeech.2021-636 | 4159-4163 | @inproceedings{paul21b_interspeech,
title = {{Knowledge Distillation for Singing Voice Detection}},
author = {Soumava Paul and Gurunath Reddy M and K. Sreenivasa Rao and Partha Pratim Das},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4159--4163},
doi = {10.21437/Interspee... | Singing Voice Detection (SVD) has been an active area of research in
music information retrieval (MIR). Currently, two deep neural network-based
methods, one based on CNN and the other on RNN, exist in literature
that learn optimized features for the voice detection (VD) task and
achieve state-of-the-art performance on... | 2011.04297 | title_snapshot |
takeda21_interspeech | Age Estimation with Speech-Age Model for Heterogeneous Speech Datasets | [
"Ryu Takeda",
"Kazunori Komatani"
] | https://www.isca-archive.org/interspeech_2021/takeda21_interspeech.html | https://www.isca-archive.org/interspeech_2021/takeda21_interspeech.pdf | 10.21437/Interspeech.2021-861 | 4164-4168 | @inproceedings{takeda21_interspeech,
title = {{Age Estimation with Speech-Age Model for Heterogeneous Speech Datasets}},
author = {Ryu Takeda and Kazunori Komatani},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4164--4168},
doi = {10.21437/Interspeech.2021-861},
issn ... | This paper describes an age estimation method from speech signals for
heterogeneous datasets. Although previous studies in the speech field
evaluate age prediction models with held-out testing data within the
same dataset recorded in a consistent setting, such evaluation does
not measure real performance. The difficult... | null | null |
teh21_interspeech | Open-Set Audio Classification with Limited Training Resources Based on Augmentation Enhanced Variational Auto-Encoder GAN with Detection-Classification Joint Training | [
"Kah Kuan Teh",
"Huy Dat Tran"
] | https://www.isca-archive.org/interspeech_2021/teh21_interspeech.html | https://www.isca-archive.org/interspeech_2021/teh21_interspeech.pdf | 10.21437/Interspeech.2021-1142 | 4169-4173 | @inproceedings{teh21_interspeech,
title = {{Open-Set Audio Classification with Limited Training Resources Based on Augmentation Enhanced Variational Auto-Encoder GAN with Detection-Classification Joint Training}},
author = {Kah Kuan Teh and Huy Dat Tran},
year = {2021},
booktitle = {{Interspeech 202... | In this paper, we propose a novel method to address practical problems
when deploying audio classification systems in operations that are
the presence of unseen sound classes (open-set) and the limitation
of training resources. To solve it, a novel method which embeds variational
auto-encoder (VAE), data augmentation a... | null | null |
fukumori21_interspeech | Deep Spectral-Cepstral Fusion for Shouted and Normal Speech Classification | [
"Takahiro Fukumori"
] | https://www.isca-archive.org/interspeech_2021/fukumori21_interspeech.html | https://www.isca-archive.org/interspeech_2021/fukumori21_interspeech.pdf | 10.21437/Interspeech.2021-1245 | 4174-4178 | @inproceedings{fukumori21_interspeech,
title = {{Deep Spectral-Cepstral Fusion for Shouted and Normal Speech Classification}},
author = {Takahiro Fukumori},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4174--4178},
doi = {10.21437/Interspeech.2021-1245},
issn = {295... | Discrimination between shouted and normal speech is crucial in audio
surveillance and monitoring. Although deep neural networks are used
in recent methods, traditional low-level speech features are applied,
such as mel-frequency cepstral coefficients and the mel spectrum. This
paper presents a deep spectral-cepstral fu... | null | null |
baghel21_interspeech | Automatic Detection of Shouted Speech Segments in Indian News Debates | [
"Shikha Baghel",
"Mrinmoy Bhattacharjee",
"S.R. Mahadeva Prasanna",
"Prithwijit Guha"
] | https://www.isca-archive.org/interspeech_2021/baghel21_interspeech.html | https://www.isca-archive.org/interspeech_2021/baghel21_interspeech.pdf | 10.21437/Interspeech.2021-1592 | 4179-4183 | @inproceedings{baghel21_interspeech,
title = {{Automatic Detection of Shouted Speech Segments in Indian News Debates}},
author = {Shikha Baghel and Mrinmoy Bhattacharjee and S.R. Mahadeva Prasanna and Prithwijit Guha},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4179--4183},
do... | Shouted speech detection is an essential pre-processing step in conventional
speech processing systems such as speech and speaker recognition, speaker
diarization, and others. Excitation source plays an important role
in shouted speech production. This work explores feature computed from
the Integrated Linear Predictio... | null | null |
gao21c_interspeech | Generalized Spoofing Detection Inspired from Audio Generation Artifacts | [
"Yang Gao",
"Tyler Vuong",
"Mahsa Elyasi",
"Gaurav Bharaj",
"Rita Singh"
] | https://www.isca-archive.org/interspeech_2021/gao21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/gao21c_interspeech.pdf | 10.21437/Interspeech.2021-1705 | 4184-4188 | @inproceedings{gao21c_interspeech,
title = {{Generalized Spoofing Detection Inspired from Audio Generation Artifacts}},
author = {Yang Gao and Tyler Vuong and Mahsa Elyasi and Gaurav Bharaj and Rita Singh},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4184--4188},
doi = {1... | State-of-the-art methods for audio generation suffer from fingerprint
artifacts and repeated inconsistencies across temporal and spectral
domains. Such artifacts could be well captured by the frequency domain
analysis over the spectrogram. Thus, we propose a novel use of long-range
spectro-temporal modulation feature —... | 2104.04111 | title_snapshot |
chen21t_interspeech | Overlapped Speech Detection Based on Spectral and Spatial Feature Fusion | [
"Weiguang Chen",
"Van Tung Pham",
"Eng Siong Chng",
"Xionghu Zhong"
] | https://www.isca-archive.org/interspeech_2021/chen21t_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21t_interspeech.pdf | 10.21437/Interspeech.2021-2138 | 4189-4193 | @inproceedings{chen21t_interspeech,
title = {{Overlapped Speech Detection Based on Spectral and Spatial Feature Fusion}},
author = {Weiguang Chen and Van Tung Pham and Eng Siong Chng and Xionghu Zhong},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4189--4193},
doi = {10.21... | Overlapped speech is widely present in conversations and can cause
significant performance degradation on speech processing such as diarization,
enhancement, and recognition. Detection of overlapped speech, in particular
when the speakers are in the far-field, is a challenging task as the
overlapped part is usually sho... | null | null |
abdullah21_interspeech | Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study | [
"Badr M. Abdullah",
"Marius Mosbach",
"Iuliia Zaitova",
"Bernd Möbius",
"Dietrich Klakow"
] | https://www.isca-archive.org/interspeech_2021/abdullah21_interspeech.html | https://www.isca-archive.org/interspeech_2021/abdullah21_interspeech.pdf | 10.21437/Interspeech.2021-678 | 4194-4198 | @inproceedings{abdullah21_interspeech,
title = {{Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study}},
author = {Badr M. Abdullah and Marius Mosbach and Iuliia Zaitova and Bernd Möbius and Dietrich Klakow},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {... | Several variants of deep neural networks have been successfully employed
for building parametric models that project variable-duration spoken
word segments onto fixed-size vector representations, or acoustic word
embeddings (AWEs). However, it remains unclear to what degree we can
rely on the distance in the emerging A... | 2106.08686 | title_snapshot |
gao21d_interspeech | Paraphrase Label Alignment for Voice Application Retrieval in Spoken Language Understanding | [
"Zheng Gao",
"Radhika Arava",
"Qian Hu",
"Xibin Gao",
"Thahir Mohamed",
"Wei Xiao",
"Mohamed AbdelHady"
] | https://www.isca-archive.org/interspeech_2021/gao21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/gao21d_interspeech.pdf | 10.21437/Interspeech.2021-97 | 4199-4203 | @inproceedings{gao21d_interspeech,
title = {{Paraphrase Label Alignment for Voice Application Retrieval in Spoken Language Understanding}},
author = {Zheng Gao and Radhika Arava and Qian Hu and Xibin Gao and Thahir Mohamed and Wei Xiao and Mohamed AbdelHady},
year = {2021},
booktitle = {{Interspeech... | Spoken language understanding (SLU) smart assistants such as Amazon
Alexa host hundreds of thousands of voice applications (skills) to
delight end-users and fulfill their utterance requests. Sometimes utterances
fail to be claimed by smart assistants due to system problems such
as model incapability or routing errors. ... | null | null |
rikhye21_interspeech | Personalized Keyphrase Detection Using Speaker and Environment Information | [
"Rajeev Rikhye",
"Quan Wang",
"Qiao Liang",
"Yanzhang He",
"Ding Zhao",
"Yiteng Huang",
"Arun Narayanan",
"Ian McGraw"
] | https://www.isca-archive.org/interspeech_2021/rikhye21_interspeech.html | https://www.isca-archive.org/interspeech_2021/rikhye21_interspeech.pdf | 10.21437/Interspeech.2021-204 | 4204-4208 | @inproceedings{rikhye21_interspeech,
title = {{Personalized Keyphrase Detection Using Speaker and Environment Information}},
author = {Rajeev Rikhye and Quan Wang and Qiao Liang and Yanzhang He and Ding Zhao and Yiteng Huang and Arun Narayanan and Ian McGraw},
year = {2021},
booktitle = {{Interspeec... | In this paper, we introduce a streaming keyphrase detection system
that can be easily customized to accurately detect any phrase composed
of words from a large vocabulary. The system is implemented with an
end-to-end trained automatic speech recognition (ASR) model and a text-independent
speaker verification model. To ... | 2104.13970 | title_snapshot |
garg21_interspeech | Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation | [
"Vineet Garg",
"Wonil Chang",
"Siddharth Sigtia",
"Saurabh Adya",
"Pramod Simha",
"Pranay Dighe",
"Chandra Dhir"
] | https://www.isca-archive.org/interspeech_2021/garg21_interspeech.html | https://www.isca-archive.org/interspeech_2021/garg21_interspeech.pdf | 10.21437/Interspeech.2021-1428 | 4209-4213 | @inproceedings{garg21_interspeech,
title = {{Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation}},
author = {Vineet Garg and Wonil Chang and Siddharth Sigtia and Saurabh Adya and Pramod Simha and Pranay Dighe and Chandra Dhir},
year = {2021},
booktitle ... | We present a unified and hardware efficient architecture for two stage
voice trigger detection (VTD) and false trigger mitigation (FTM) tasks.
Two stage VTD systems of voice assistants can get falsely activated
to audio segments acoustically similar to the trigger phrase of interest.
FTM systems cancel such activations... | 2105.06598 | title_snapshot |
mazumder21_interspeech | Few-Shot Keyword Spotting in Any Language | [
"Mark Mazumder",
"Colby Banbury",
"Josh Meyer",
"Pete Warden",
"Vijay Janapa Reddi"
] | https://www.isca-archive.org/interspeech_2021/mazumder21_interspeech.html | https://www.isca-archive.org/interspeech_2021/mazumder21_interspeech.pdf | 10.21437/Interspeech.2021-1966 | 4214-4218 | @inproceedings{mazumder21_interspeech,
title = {{Few-Shot Keyword Spotting in Any Language}},
author = {Mark Mazumder and Colby Banbury and Josh Meyer and Pete Warden and Vijay Janapa Reddi},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4214--4218},
doi = {10.21437/Intersp... | We introduce a few-shot transfer learning method for keyword spotting
in any language. Leveraging open speech corpora in nine languages,
we automate the extraction of a large multilingual keyword bank and
use it to train an embedding model. With just five training examples,
we fine-tune the embedding model for keyword ... | 2104.01454 | title_snapshot |
wang21da_interspeech | Text Anchor Based Metric Learning for Small-Footprint Keyword Spotting | [
"Li Wang",
"Rongzhi Gu",
"Nuo Chen",
"Yuexian Zou"
] | https://www.isca-archive.org/interspeech_2021/wang21da_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21da_interspeech.pdf | 10.21437/Interspeech.2021-136 | 4219-4223 | @inproceedings{wang21da_interspeech,
title = {{Text Anchor Based Metric Learning for Small-Footprint Keyword Spotting}},
author = {Li Wang and Rongzhi Gu and Nuo Chen and Yuexian Zou},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4219--4223},
doi = {10.21437/Interspeech.20... | Keyword Spotting (KWS) remains challenging to achieve the trade-off
between small footprint and high accuracy. Recently proposed metric
learning approaches improved the generalizability of models for the
KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts
(SOTA) in terms of model size. However, fo... | 2108.05516 | title_snapshot |
chen21u_interspeech | A Meta-Learning Approach for User-Defined Spoken Term Classification with Varying Classes and Examples | [
"Yangbin Chen",
"Tom Ko",
"Jianping Wang"
] | https://www.isca-archive.org/interspeech_2021/chen21u_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21u_interspeech.pdf | 10.21437/Interspeech.2021-147 | 4224-4228 | @inproceedings{chen21u_interspeech,
title = {{A Meta-Learning Approach for User-Defined Spoken Term Classification with Varying Classes and Examples}},
author = {Yangbin Chen and Tom Ko and Jianping Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4224--4228},
doi = {10... | Recently we formulated a user-defined spoken term classification task
as a few-shot learning task and tackled the task using Model-Agnostic
Meta-Learning (MAML) algorithm. Our results show that the meta-learning
approach performs much better than conventional supervised learning
and transfer learning in the task, espec... | null | null |
lee21g_interspeech | Auxiliary Sequence Labeling Tasks for Disfluency Detection | [
"Dongyub Lee",
"Byeongil Ko",
"Myeong Cheol Shin",
"Taesun Whang",
"Daniel Lee",
"Eunhwa Kim",
"Eunggyun Kim",
"Jaechoon Jo"
] | https://www.isca-archive.org/interspeech_2021/lee21g_interspeech.html | https://www.isca-archive.org/interspeech_2021/lee21g_interspeech.pdf | 10.21437/Interspeech.2021-400 | 4229-4233 | @inproceedings{lee21g_interspeech,
title = {{Auxiliary Sequence Labeling Tasks for Disfluency Detection}},
author = {Dongyub Lee and Byeongil Ko and Myeong Cheol Shin and Taesun Whang and Daniel Lee and Eunhwa Kim and Eunggyun Kim and Jaechoon Jo},
year = {2021},
booktitle = {{Interspeech 2021}},
... | Detecting disfluencies in spontaneous speech is an important preprocessing
step in natural language processing and speech recognition applications.
Existing works for disfluency detection have focused on designing a
single objective only for disfluency detection, while auxiliary objectives
utilizing linguistic informat... | 2011.04512 | title_snapshot |
zhou21g_interspeech | Energy-Friendly Keyword Spotting System Using Add-Based Convolution | [
"Hang Zhou",
"Wenchao Hu",
"Yu Ting Yeung",
"Xiao Chen"
] | https://www.isca-archive.org/interspeech_2021/zhou21g_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhou21g_interspeech.pdf | 10.21437/Interspeech.2021-458 | 4234-4238 | @inproceedings{zhou21g_interspeech,
title = {{Energy-Friendly Keyword Spotting System Using Add-Based Convolution}},
author = {Hang Zhou and Wenchao Hu and Yu Ting Yeung and Xiao Chen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4234--4238},
doi = {10.21437/Interspeech.2... | Wake-up keyword of a keyword spotting (KWS) system represents brand
name of a smart device. Performance of KWS is also crucial for modern
speech based human-device interaction. An on-device KWS with both high
accuracy and low power consumption is desired. We propose a KWS with
add-based convolution layers, namely Add T... | null | null |
jia21b_interspeech | The 2020 Personalized Voice Trigger Challenge: Open Datasets, Evaluation Metrics, Baseline System and Results | [
"Yan Jia",
"Xingming Wang",
"Xiaoyi Qin",
"Yinping Zhang",
"Xuyang Wang",
"Junjie Wang",
"Dong Zhang",
"Ming Li"
] | https://www.isca-archive.org/interspeech_2021/jia21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/jia21b_interspeech.pdf | 10.21437/Interspeech.2021-602 | 4239-4243 | @inproceedings{jia21b_interspeech,
title = {{The 2020 Personalized Voice Trigger Challenge: Open Datasets, Evaluation Metrics, Baseline System and Results}},
author = {Yan Jia and Xingming Wang and Xiaoyi Qin and Yinping Zhang and Xuyang Wang and Junjie Wang and Dong Zhang and Ming Li},
year = {2021},... | The 2020 Personalized Voice Trigger Challenge (PVTC2020) addresses
two different research problems in a unified setup: joint wake-up word
detection with speaker verification on close-talking single microphone
data and far-field multi-channel microphone array data. Specially,
the second task poses an additional cross-ch... | 2101.01935 | title_judge |
wang21ea_interspeech | Auto-KWS 2021 Challenge: Task, Datasets, and Baselines | [
"Jingsong Wang",
"Yuxuan He",
"Chunyu Zhao",
"Qijie Shao",
"Wei-Wei Tu",
"Tom Ko",
"Hung-yi Lee",
"Lei Xie"
] | https://www.isca-archive.org/interspeech_2021/wang21ea_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21ea_interspeech.pdf | 10.21437/Interspeech.2021-817 | 4244-4248 | @inproceedings{wang21ea_interspeech,
title = {{Auto-KWS 2021 Challenge: Task, Datasets, and Baselines}},
author = {Jingsong Wang and Yuxuan He and Chunyu Zhao and Qijie Shao and Wei-Wei Tu and Tom Ko and Hung-yi Lee and Lei Xie},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4244--... | Auto-KWS 2021 challenge calls for automated machine learning (AutoML)
solutions to automate the process of applying machine learning to a
customized keyword spotting task. Compared with other keyword spotting
tasks, Auto-KWS challenge has the following three characteristics:
1) The challenge focuses on the problem of c... | 2104.00513 | title_snapshot |
berg21_interspeech | Keyword Transformer: A Self-Attention Model for Keyword Spotting | [
"Axel Berg",
"Mark O’Connor",
"Miguel Tairum Cruz"
] | https://www.isca-archive.org/interspeech_2021/berg21_interspeech.html | https://www.isca-archive.org/interspeech_2021/berg21_interspeech.pdf | 10.21437/Interspeech.2021-1286 | 4249-4253 | @inproceedings{berg21_interspeech,
title = {{Keyword Transformer: A Self-Attention Model for Keyword Spotting}},
author = {Axel Berg and Mark O’Connor and Miguel Tairum Cruz},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4249--4253},
doi = {10.21437/Interspeech.2021-1286},... | The Transformer architecture has been successful across many domains,
including natural language processing, computer vision and speech recognition.
In keyword spotting, self-attention has primarily been used on top
of convolutional or recurrent encoders. We investigate a range of ways
to adapt the Transformer architec... | 2104.00769 | title_snapshot |
awasthi21_interspeech | Teaching Keyword Spotters to Spot New Keywords with Limited Examples | [
"Abhijeet Awasthi",
"Kevin Kilgour",
"Hassan Rom"
] | https://www.isca-archive.org/interspeech_2021/awasthi21_interspeech.html | https://www.isca-archive.org/interspeech_2021/awasthi21_interspeech.pdf | 10.21437/Interspeech.2021-1395 | 4254-4258 | @inproceedings{awasthi21_interspeech,
title = {{Teaching Keyword Spotters to Spot New Keywords with Limited Examples}},
author = {Abhijeet Awasthi and Kevin Kilgour and Hassan Rom},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4254--4258},
doi = {10.21437/Interspeech.2021-... | Learning to recognize new keywords with just a few examples is essential
for personalizing keyword spotting (KWS) models to a user’s choice
of keywords. However, modern KWS models are typically trained on large
datasets and restricted to a small vocabulary of keywords, limiting
their transferability to a broad range of... | 2106.02443 | title_snapshot |
wang21fa_interspeech | A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection | [
"Xin Wang",
"Junichi Yamagishi"
] | https://www.isca-archive.org/interspeech_2021/wang21fa_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21fa_interspeech.pdf | 10.21437/Interspeech.2021-702 | 4259-4263 | @inproceedings{wang21fa_interspeech,
title = {{A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection}},
author = {Xin Wang and Junichi Yamagishi},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4259--4263},
doi = {10.21437/Interspeech.2... | A great deal of recent research effort on speech spoofing countermeasures
has been invested into back-end neural networks and training criteria.
We contribute to this effort with a comparative perspective in this
study. Our comparison of countermeasure models on the ASVspoof 2019
logical access scenario takes into acco... | 2103.11326 | title_snapshot |
zhang21ca_interspeech | An Initial Investigation for Detecting Partially Spoofed Audio | [
"Lin Zhang",
"Xin Wang",
"Erica Cooper",
"Junichi Yamagishi",
"Jose Patino",
"Nicholas Evans"
] | https://www.isca-archive.org/interspeech_2021/zhang21ca_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21ca_interspeech.pdf | 10.21437/Interspeech.2021-738 | 4264-4268 | @inproceedings{zhang21ca_interspeech,
title = {{An Initial Investigation for Detecting Partially Spoofed Audio}},
author = {Lin Zhang and Xin Wang and Erica Cooper and Junichi Yamagishi and Jose Patino and Nicholas Evans},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4264--4268},
... | All existing databases of spoofed speech contain attack data that is
spoofed in its entirety. In practice, it is entirely plausible that
successful attacks can be mounted with utterances that are only partially
spoofed. By definition, partially-spoofed utterances contain a mix
of both spoofed and bona fide segments, wh... | 2104.02518 | title_snapshot |
xie21_interspeech | Siamese Network with wav2vec Feature for Spoofing Speech Detection | [
"Yang Xie",
"Zhenchuan Zhang",
"Yingchun Yang"
] | https://www.isca-archive.org/interspeech_2021/xie21_interspeech.html | https://www.isca-archive.org/interspeech_2021/xie21_interspeech.pdf | 10.21437/Interspeech.2021-847 | 4269-4273 | @inproceedings{xie21_interspeech,
title = {{Siamese Network with wav2vec Feature for Spoofing Speech Detection}},
author = {Yang Xie and Zhenchuan Zhang and Yingchun Yang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4269--4273},
doi = {10.21437/Interspeech.2021-847},
i... | Automatic speaker verification is vulnerable to spoofing attacks with
synthesized or converted speech. Although high-performance anti-spoofing
countermeasures can achieve high accuracy when the training and testing
spoofing attack examples are similarly distributed, their performance
degrades significantly when confron... | null | null |
cheng21b_interspeech | Cross-Database Replay Detection in Terminal-Dependent Speaker Verification | [
"Xingliang Cheng",
"Mingxing Xu",
"Thomas Fang Zheng"
] | https://www.isca-archive.org/interspeech_2021/cheng21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/cheng21b_interspeech.pdf | 10.21437/Interspeech.2021-960 | 4274-4278 | @inproceedings{cheng21b_interspeech,
title = {{Cross-Database Replay Detection in Terminal-Dependent Speaker Verification}},
author = {Xingliang Cheng and Mingxing Xu and Thomas Fang Zheng},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4274--4278},
doi = {10.21437/Interspe... | The vulnerability of automatic speaker verification (ASV) systems against
replay attacks becomes a severe problem. Although various methods have
been proposed for replay detection, the generalization capability is
still limited. For instance, a detection model trained on one database
may fully fail when tested on anoth... | null | null |
zhang21da_interspeech | The Effect of Silence and Dual-Band Fusion in Anti-Spoofing System | [
"Yuxiang Zhang",
"Wenchao Wang",
"Pengyuan Zhang"
] | https://www.isca-archive.org/interspeech_2021/zhang21da_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21da_interspeech.pdf | 10.21437/Interspeech.2021-1281 | 4279-4283 | @inproceedings{zhang21da_interspeech,
title = {{The Effect of Silence and Dual-Band Fusion in Anti-Spoofing System}},
author = {Yuxiang Zhang and Wenchao Wang and Pengyuan Zhang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4279--4283},
doi = {10.21437/Interspeech.2021-12... | The current neural network based anti-spoofing systems have poor robustness.
Their performance degrades further after voice activity detection (VAD)
performed, making it difficult to be applied in practice. This work
investigated the effect of silence at the beginning and end of speech,
finding that silent differences ... | null | null |
peng21d_interspeech | Pairing Weak with Strong: Twin Models for Defending Against Adversarial Attack on Speaker Verification | [
"Zhiyuan Peng",
"Xu Li",
"Tan Lee"
] | https://www.isca-archive.org/interspeech_2021/peng21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/peng21d_interspeech.pdf | 10.21437/Interspeech.2021-1343 | 4284-4288 | @inproceedings{peng21d_interspeech,
title = {{Pairing Weak with Strong: Twin Models for Defending Against Adversarial Attack on Speaker Verification}},
author = {Zhiyuan Peng and Xu Li and Tan Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4284--4288},
doi = {10.21437/... | Vulnerability of speaker verification (SV) systems under adversarial
attack receives wide attention recently. Simple and effective countermeasures
against such attack are yet to be developed. This paper formulates
the task of adversarial defense as a problem of attack detection. The
detection is made possible with the ... | null | null |
ling21_interspeech | Attention-Based Convolutional Neural Network for ASV Spoofing Detection | [
"Hefei Ling",
"Leichao Huang",
"Junrui Huang",
"Baiyan Zhang",
"Ping Li"
] | https://www.isca-archive.org/interspeech_2021/ling21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ling21_interspeech.pdf | 10.21437/Interspeech.2021-1404 | 4289-4293 | @inproceedings{ling21_interspeech,
title = {{Attention-Based Convolutional Neural Network for ASV Spoofing Detection}},
author = {Hefei Ling and Leichao Huang and Junrui Huang and Baiyan Zhang and Ping Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4289--4293},
doi = {1... | In recent years, automatic speaker verification (ASV) algorithms have
undergone significant progress. They have been widely deployed in different
applications, but the ASV systems are vulnerable to spoofing attacks,
such as impersonation, replay, text-to-speech, voice conversion and
the recently emerged adversarial att... | null | null |
wu21i_interspeech | Voting for the Right Answer: Adversarial Defense for Speaker Verification | [
"Haibin Wu",
"Yang Zhang",
"Zhiyong Wu",
"Dong Wang",
"Hung-yi Lee"
] | https://www.isca-archive.org/interspeech_2021/wu21i_interspeech.html | https://www.isca-archive.org/interspeech_2021/wu21i_interspeech.pdf | 10.21437/Interspeech.2021-1452 | 4294-4298 | @inproceedings{wu21i_interspeech,
title = {{Voting for the Right Answer: Adversarial Defense for Speaker Verification}},
author = {Haibin Wu and Yang Zhang and Zhiyong Wu and Dong Wang and Hung-yi Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4294--4298},
doi = {10.21... | Automatic speaker verification (ASV) is a well developed technology
for biometric identification, and has been ubiquitous implemented in
security-critic applications, such as banking and access control. However,
previous works have shown that ASV is under the radar of adversarial
attacks, which are very similar to thei... | 2106.07868 | title_snapshot |
kinnunen21_interspeech | Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing | [
"Tomi Kinnunen",
"Andreas Nautsch",
"Md. Sahidullah",
"Nicholas Evans",
"Xin Wang",
"Massimiliano Todisco",
"Héctor Delgado",
"Junichi Yamagishi",
"Kong Aik Lee"
] | https://www.isca-archive.org/interspeech_2021/kinnunen21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kinnunen21_interspeech.pdf | 10.21437/Interspeech.2021-1522 | 4299-4303 | @inproceedings{kinnunen21_interspeech,
title = {{Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing}},
author = {Tomi Kinnunen and Andreas Nautsch and Md. Sahidullah and Nicholas Evans and Xin Wang and Massimiliano Todisco and Héctor Delgado and Junichi Y... | Whether it be for results summarization, or the analysis of classifier
fusion, some means to compare different classifiers can often provide
illuminating insight into their behaviour, (dis)similarity or complementarity.
We propose a simple method to derive 2D representation from detection
scores produced by an arbitrar... | 2106.06362 | title_snapshot |
villalba21_interspeech | Representation Learning to Classify and Detect Adversarial Attacks Against Speaker and Speech Recognition Systems | [
"Jesús Villalba",
"Sonal Joshi",
"Piotr Żelasko",
"Najim Dehak"
] | https://www.isca-archive.org/interspeech_2021/villalba21_interspeech.html | https://www.isca-archive.org/interspeech_2021/villalba21_interspeech.pdf | 10.21437/Interspeech.2021-1759 | 4304-4308 | @inproceedings{villalba21_interspeech,
title = {{Representation Learning to Classify and Detect Adversarial Attacks Against Speaker and Speech Recognition Systems}},
author = {Jesús Villalba and Sonal Joshi and Piotr Żelasko and Najim Dehak},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | Adversarial attacks have become a major threat for machine learning
applications. There is a growing interest in studying these attacks
in the audio domain, e.g, speech and speaker recognition; and find
defenses against them. In this work, we focus on using representation
learning to classify/detect attacks w.r.t. the ... | 2107.04448 | title_snapshot |
zhang21ea_interspeech | An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems | [
"You Zhang",
"Ge Zhu",
"Fei Jiang",
"Zhiyao Duan"
] | https://www.isca-archive.org/interspeech_2021/zhang21ea_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21ea_interspeech.pdf | 10.21437/Interspeech.2021-1820 | 4309-4313 | @inproceedings{zhang21ea_interspeech,
title = {{An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems}},
author = {You Zhang and Ge Zhu and Fei Jiang and Zhiyao Duan},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4309--4313},
doi = {10.2... | Spoofing countermeasure (CM) systems are critical in speaker verification;
they aim to discern spoofing attacks from bona fide speech trials.
In practice, however, acoustic condition variability in speech utterances
may significantly degrade the performance of CM systems. In this paper,
we conduct a cross-dataset study... | 2104.01320 | title_snapshot |
li21o_interspeech | Channel-Wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks | [
"Xu Li",
"Xixin Wu",
"Hui Lu",
"Xunying Liu",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2021/li21o_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21o_interspeech.pdf | 10.21437/Interspeech.2021-2125 | 4314-4318 | @inproceedings{li21o_interspeech,
title = {{Channel-Wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks}},
author = {Xu Li and Xixin Wu and Hui Lu and Xunying Liu and Helen Meng},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4314--4318},
doi = {10.2143... | Existing approaches for anti-spoofing in automatic speaker verification
(ASV) still lack generalizability to unseen attacks. The Res2Net approach
designs a residual-like connection between feature groups within one
block, which increases the possible receptive fields and improves the
system’s detection generalizability... | 2107.08803 | title_snapshot |
ge21c_interspeech | Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection | [
"Wanying Ge",
"Michele Panariello",
"Jose Patino",
"Massimiliano Todisco",
"Nicholas Evans"
] | https://www.isca-archive.org/interspeech_2021/ge21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/ge21c_interspeech.pdf | 10.21437/Interspeech.2021-1187 | 4319-4323 | @inproceedings{ge21c_interspeech,
title = {{Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection}},
author = {Wanying Ge and Michele Panariello and Jose Patino and Massimiliano Todisco and Nicholas Evans},
year = {2021},
booktitle = {{Interspeech 2021}},
pages... | This paper reports the first successful application of a differentiable
architecture search (DARTS) approach to the deepfake and spoofing detection
problems. An example of neural architecture search, DARTS operates
upon a continuous, differentiable search space which enables both the
architecture and parameters to be o... | 2104.03123 | title_snapshot |
peterson21_interspeech | OpenASR20: An Open Challenge for Automatic Speech Recognition of Conversational Telephone Speech in Low-Resource Languages | [
"Kay Peterson",
"Audrey Tong",
"Yan Yu"
] | https://www.isca-archive.org/interspeech_2021/peterson21_interspeech.html | https://www.isca-archive.org/interspeech_2021/peterson21_interspeech.pdf | 10.21437/Interspeech.2021-1930 | 4324-4328 | @inproceedings{peterson21_interspeech,
title = {{OpenASR20: An Open Challenge for Automatic Speech Recognition of Conversational Telephone Speech in Low-Resource Languages}},
author = {Kay Peterson and Audrey Tong and Yan Yu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4324--432... | In 2020, the National Institute of Standards and Technology (NIST),
in cooperation with the Intelligence Advanced Research Project Activity
(IARPA), conducted an open challenge on automatic speech recognition
(ASR) technology for low-resource languages on a challenging data type
— conversational telephone speech. The O... | null | null |
madikeri21_interspeech | Multitask Adaptation with Lattice-Free MMI for Multi-Genre Speech Recognition of Low Resource Languages | [
"Srikanth Madikeri",
"Petr Motlicek",
"Hervé Bourlard"
] | https://www.isca-archive.org/interspeech_2021/madikeri21_interspeech.html | https://www.isca-archive.org/interspeech_2021/madikeri21_interspeech.pdf | 10.21437/Interspeech.2021-1778 | 4329-4333 | @inproceedings{madikeri21_interspeech,
title = {{Multitask Adaptation with Lattice-Free MMI for Multi-Genre Speech Recognition of Low Resource Languages}},
author = {Srikanth Madikeri and Petr Motlicek and Hervé Bourlard},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4329--4333},
... | In this paper, we develop Automatic Speech Recognition (ASR) systems
for multi-genre speech recognition of low-resource languages where
training data is predominantly conversational speech but test data
can be in one of the following genres: news broadcast, topical broadcast
and conversational speech. ASR for low-resou... | null | null |
zhu21f_interspeech | An Improved Wav2Vec 2.0 Pre-Training Approach Using Enhanced Local Dependency Modeling for Speech Recognition | [
"Qiu-shi Zhu",
"Jie Zhang",
"Ming-hui Wu",
"Xin Fang",
"Li-Rong Dai"
] | https://www.isca-archive.org/interspeech_2021/zhu21f_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhu21f_interspeech.pdf | 10.21437/Interspeech.2021-67 | 4334-4338 | @inproceedings{zhu21f_interspeech,
title = {{An Improved Wav2Vec 2.0 Pre-Training Approach Using Enhanced Local Dependency Modeling for Speech Recognition}},
author = {Qiu-shi Zhu and Jie Zhang and Ming-hui Wu and Xin Fang and Li-Rong Dai},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | wav2vec 2.0 is a recently proposed self-supervised pre-training framework
for learning speech representation. It utilizes a transformer to learn
global contextual representation, which is effective especially in
low-resource scenarios. Besides, it was shown that combining convolution
neural network and transformer to m... | null | null |
lin21i_interspeech | Systems for Low-Resource Speech Recognition Tasks in Open Automatic Speech Recognition and Formosa Speech Recognition Challenges | [
"Hung-Pang Lin",
"Yu-Jia Zhang",
"Chia-Ping Chen"
] | https://www.isca-archive.org/interspeech_2021/lin21i_interspeech.html | https://www.isca-archive.org/interspeech_2021/lin21i_interspeech.pdf | 10.21437/Interspeech.2021-358 | 4339-4343 | @inproceedings{lin21i_interspeech,
title = {{Systems for Low-Resource Speech Recognition Tasks in Open Automatic Speech Recognition and Formosa Speech Recognition Challenges}},
author = {Hung-Pang Lin and Yu-Jia Zhang and Chia-Ping Chen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | We, in the team name of NSYSU-MITLab, have participated in low-resource
speech recognition of the Open Automatic Speech Recognition Challenge
2020 (OpenASR20) and Formosa Speech Recognition Challenge 2020 (FSR-2020).
For the tasks in the challenges, we build and compare end-to-end (E2E)
systems and Deep Neural Network ... | null | null |
zhao21c_interspeech | The TNT Team System Descriptions of Cantonese and Mongolian for IARPA OpenASR20 | [
"Jing Zhao",
"Zhiqiang Lv",
"Ambyera Han",
"Guan-Bo Wang",
"Guixin Shi",
"Jian Kang",
"Jinghao Yan",
"Pengfei Hu",
"Shen Huang",
"Wei-Qiang Zhang"
] | https://www.isca-archive.org/interspeech_2021/zhao21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhao21c_interspeech.pdf | 10.21437/Interspeech.2021-1063 | 4344-4348 | @inproceedings{zhao21c_interspeech,
title = {{The TNT Team System Descriptions of Cantonese and Mongolian for IARPA OpenASR20}},
author = {Jing Zhao and Zhiqiang Lv and Ambyera Han and Guan-Bo Wang and Guixin Shi and Jian Kang and Jinghao Yan and Pengfei Hu and Shen Huang and Wei-Qiang Zhang},
year = ... | This paper presents our work for OpenASR20 Challenge. We describe our
Automatic Speech Recognition (ASR) systems for Cantonese and Mongolian
under both constrained and unconstrained conditions. For constrained
condition, a hybrid NN-HMM ASR system play the main role, while for
unconstrained condition, an end-to-end ASR... | null | null |
alumae21_interspeech | Combining Hybrid and End-to-End Approaches for the OpenASR20 Challenge | [
"Tanel Alumäe",
"Jiaming Kong"
] | https://www.isca-archive.org/interspeech_2021/alumae21_interspeech.html | https://www.isca-archive.org/interspeech_2021/alumae21_interspeech.pdf | 10.21437/Interspeech.2021-1086 | 4349-4353 | @inproceedings{alumae21_interspeech,
title = {{Combining Hybrid and End-to-End Approaches for the OpenASR20 Challenge}},
author = {Tanel Alumäe and Jiaming Kong},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4349--4353},
doi = {10.21437/Interspeech.2021-1086},
issn ... | This paper describes the TalTech team submission to the OpenASR20 Challenge.
OpenASR20 evaluated low-resource speech recognition technologies across
10 languages, using only 10 hours of training data in the constrained
condition. Our ASR systems used hybrid CNN-TDNNF-based acoustic models,
trained with different data a... | null | null |
morris21_interspeech | One Size Does Not Fit All in Resource-Constrained ASR | [
"Ethan Morris",
"Robbie Jimerson",
"Emily Prud’hommeaux"
] | https://www.isca-archive.org/interspeech_2021/morris21_interspeech.html | https://www.isca-archive.org/interspeech_2021/morris21_interspeech.pdf | 10.21437/Interspeech.2021-1970 | 4354-4358 | @inproceedings{morris21_interspeech,
title = {{One Size Does Not Fit All in Resource-Constrained ASR}},
author = {Ethan Morris and Robbie Jimerson and Emily Prud’hommeaux},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4354--4358},
doi = {10.21437/Interspeech.2021-1970},
... | The application of deep neural networks to the task of acoustic modeling
for automatic speech recognition has resulted in dramatic decreases
in ASR word error rates, enabling the use of this technology for interacting
with smart phones and personal home assistants in high-resource languages.
Developing ASR models of th... | null | null |
gimeno21_interspeech | Unsupervised Representation Learning for Speech Activity Detection in the Fearless Steps Challenge 2021 | [
"Pablo Gimeno",
"Alfonso Ortega",
"Antonio Miguel",
"Eduardo Lleida"
] | https://www.isca-archive.org/interspeech_2021/gimeno21_interspeech.html | https://www.isca-archive.org/interspeech_2021/gimeno21_interspeech.pdf | 10.21437/Interspeech.2021-309 | 4359-4363 | @inproceedings{gimeno21_interspeech,
title = {{Unsupervised Representation Learning for Speech Activity Detection in the Fearless Steps Challenge 2021}},
author = {Pablo Gimeno and Alfonso Ortega and Antonio Miguel and Eduardo Lleida},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {... | In this paper, we describe the ViVoLab speech activity detection (SAD)
system submitted to the Fearless Steps Challenge Phase III. This series
of challenges have proposed a number of speech processing task dealing
with audio from Apollo space missions over the last few years. The
focus in this edition is set on the gen... | null | null |
vuong21_interspeech | The Application of Learnable STRF Kernels to the 2021 Fearless Steps Phase-03 SAD Challenge | [
"Tyler Vuong",
"Yangyang Xia",
"Richard M. Stern"
] | https://www.isca-archive.org/interspeech_2021/vuong21_interspeech.html | https://www.isca-archive.org/interspeech_2021/vuong21_interspeech.pdf | 10.21437/Interspeech.2021-651 | 4364-4368 | @inproceedings{vuong21_interspeech,
title = {{The Application of Learnable STRF Kernels to the 2021 Fearless Steps Phase-03 SAD Challenge}},
author = {Tyler Vuong and Yangyang Xia and Richard M. Stern},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4364--4368},
doi = {10.21... | We describe a deep-learning-based system developed for the Fearless
Steps Phase-03 Speech Activity Detection (SAD) challenge. The system
includes both learnable spectro-temporal receptive fields (STRFs) and
unconstrained 2-dimensional convolutional kernels in the first layer.
Experiments show that the inclusion of lear... | null | null |
sarfjoo21_interspeech | Speech Activity Detection Based on Multilingual Speech Recognition System | [
"Seyyed Saeed Sarfjoo",
"Srikanth Madikeri",
"Petr Motlicek"
] | https://www.isca-archive.org/interspeech_2021/sarfjoo21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sarfjoo21_interspeech.pdf | 10.21437/Interspeech.2021-1058 | 4369-4373 | @inproceedings{sarfjoo21_interspeech,
title = {{Speech Activity Detection Based on Multilingual Speech Recognition System}},
author = {Seyyed Saeed Sarfjoo and Srikanth Madikeri and Petr Motlicek},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4369--4373},
doi = {10.21437/I... | To better model the contextual information and increase the generalization
ability of the Speech Activity Detection (SAD) system, this paper leverages
a multilingual Automatic Speech Recognition (ASR) system to perform
SAD. Sequence-discriminative training of Acoustic Model (AM) using
Lattice-Free Maximum Mutual Inform... | 2010.12277 | title_snapshot |
luckenbaugh21_interspeech | Voice Activity Detection with Teacher-Student Domain Emulation | [
"Jarrod Luckenbaugh",
"Samuel Abplanalp",
"Rachel Gonzalez",
"Daniel Fulford",
"David Gard",
"Carlos Busso"
] | https://www.isca-archive.org/interspeech_2021/luckenbaugh21_interspeech.html | https://www.isca-archive.org/interspeech_2021/luckenbaugh21_interspeech.pdf | 10.21437/Interspeech.2021-1234 | 4374-4378 | @inproceedings{luckenbaugh21_interspeech,
title = {{Voice Activity Detection with Teacher-Student Domain Emulation}},
author = {Jarrod Luckenbaugh and Samuel Abplanalp and Rachel Gonzalez and Daniel Fulford and David Gard and Carlos Busso},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | Transfer learning is a promising approach to increase performance for
many speech-based systems, including voice activity detection (VAD). Domain adaptation, a subfield of transfer learning, often improves
model conditioning in the presence of a mismatch between train-test
conditions. This study proposes a formulation ... | null | null |
ghahabi21_interspeech | EML Online Speech Activity Detection for the Fearless Steps Challenge Phase-III | [
"Omid Ghahabi",
"Volker Fischer"
] | https://www.isca-archive.org/interspeech_2021/ghahabi21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ghahabi21_interspeech.pdf | 10.21437/Interspeech.2021-1456 | 4379-4382 | @inproceedings{ghahabi21_interspeech,
title = {{EML Online Speech Activity Detection for the Fearless Steps Challenge Phase-III}},
author = {Omid Ghahabi and Volker Fischer},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4379--4382},
doi = {10.21437/Interspeech.2021-1456},
... | Speech Activity Detection (SAD), locating speech segments within an
audio recording, is a main part of most speech technology applications.
Robust SAD is usually more difficult in noisy conditions with varying
signal-to-noise ratios (SNR). The Fearless Steps challenge has recently
provided such data from the NASA Apoll... | 2106.11075 | title_snapshot |
opatka21_interspeech | Device Playback Augmentation with Echo Cancellation for Keyword Spotting | [
"Kuba Łopatka",
"Katarzyna Kaszuba-Miotke",
"Piotr Klinke",
"Paweł Trella"
] | https://www.isca-archive.org/interspeech_2021/opatka21_interspeech.html | https://www.isca-archive.org/interspeech_2021/opatka21_interspeech.pdf | 10.21437/Interspeech.2021-1316 | 4383-4387 | @inproceedings{opatka21_interspeech,
title = {{Device Playback Augmentation with Echo Cancellation for Keyword Spotting}},
author = {Kuba Łopatka and Katarzyna Kaszuba-Miotke and Piotr Klinke and Paweł Trella},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4383--4387},
doi ... | Keyword spotting (KWS) is required to operate in device playback conditions
in which the device itself plays interfering signals. We propose a
new method to augment the training set and adapt the acoustic model
to the playback environment. It is based on acoustic simulation which
models the coupling between the device’... | null | null |
yusuf21_interspeech | End-to-End Open Vocabulary Keyword Search | [
"Bolaji Yusuf",
"Alican Gok",
"Batuhan Gundogdu",
"Murat Saraclar"
] | https://www.isca-archive.org/interspeech_2021/yusuf21_interspeech.html | https://www.isca-archive.org/interspeech_2021/yusuf21_interspeech.pdf | 10.21437/Interspeech.2021-1399 | 4388-4392 | @inproceedings{yusuf21_interspeech,
title = {{End-to-End Open Vocabulary Keyword Search}},
author = {Bolaji Yusuf and Alican Gok and Batuhan Gundogdu and Murat Saraclar},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4388--4392},
doi = {10.21437/Interspeech.2021-1399},
is... | Recently, neural approaches to spoken content retrieval have become
popular. However, they tend to be restricted in their vocabulary or
in their ability to deal with imbalanced test settings. These restrictions
limit their applicability in keyword search, where the set of queries
is not known beforehand, and where the ... | 2108.10357 | title_snapshot |
merkx21_interspeech | Semantic Sentence Similarity: Size does not Always Matter | [
"Danny Merkx",
"Stefan L. Frank",
"Mirjam Ernestus"
] | https://www.isca-archive.org/interspeech_2021/merkx21_interspeech.html | https://www.isca-archive.org/interspeech_2021/merkx21_interspeech.pdf | 10.21437/Interspeech.2021-1464 | 4393-4397 | @inproceedings{merkx21_interspeech,
title = {{Semantic Sentence Similarity: Size does not Always Matter}},
author = {Danny Merkx and Stefan L. Frank and Mirjam Ernestus},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4393--4397},
doi = {10.21437/Interspeech.2021-1464},
is... | This study addresses the question whether visually grounded speech
recognition (VGS) models learn to capture sentence semantics without
access to any prior linguistic knowledge. We produce synthetic and
natural spoken versions of a well known semantic textual similarity
database and show that our VGS model produces emb... | 2106.08648 | title_snapshot |
svec21_interspeech | Spoken Term Detection and Relevance Score Estimation Using Dot-Product of Pronunciation Embeddings | [
"Jan Švec",
"Luboš Šmídl",
"Josef V. Psutka",
"Aleš Pražák"
] | https://www.isca-archive.org/interspeech_2021/svec21_interspeech.html | https://www.isca-archive.org/interspeech_2021/svec21_interspeech.pdf | 10.21437/Interspeech.2021-1704 | 4398-4402 | @inproceedings{svec21_interspeech,
title = {{Spoken Term Detection and Relevance Score Estimation Using Dot-Product of Pronunciation Embeddings}},
author = {Jan Švec and Luboš Šmídl and Josef V. Psutka and Aleš Pražák},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4398--4402},
d... | The paper describes a novel approach to Spoken Term Detection (STD)
in large spoken archives using deep LSTM networks. The work is based
on the previous approach of using Siamese neural networks for STD and
naturally extends it to directly localize a spoken term and estimate
its relevance score. The phoneme confusion n... | 2210.11895 | title_snapshot |
buet21_interspeech | Toward Genre Adapted Closed Captioning | [
"François Buet",
"François Yvon"
] | https://www.isca-archive.org/interspeech_2021/buet21_interspeech.html | https://www.isca-archive.org/interspeech_2021/buet21_interspeech.pdf | 10.21437/Interspeech.2021-1762 | 4403-4407 | @inproceedings{buet21_interspeech,
title = {{Toward Genre Adapted Closed Captioning}},
author = {François Buet and François Yvon},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4403--4407},
doi = {10.21437/Interspeech.2021-1762},
issn = {2958-1796},
} | This paper studies the generation of intralingual closed captions from
automatic speech transcripts, with the aim to assess techniques for
multi-genre captioning. Captions and subtitles greatly vary in form
and content depending on the programs genres and subtitling styles,
resulting for instance in significantly diffe... | null | null |
korzekwa21b_interspeech | Weakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech | [
"Daniel Korzekwa",
"Jaime Lorenzo-Trueba",
"Thomas Drugman",
"Shira Calamaro",
"Bozena Kostek"
] | https://www.isca-archive.org/interspeech_2021/korzekwa21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/korzekwa21b_interspeech.pdf | 10.21437/Interspeech.2021-38 | 4408-4412 | @inproceedings{korzekwa21b_interspeech,
title = {{Weakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech}},
author = {Daniel Korzekwa and Jaime Lorenzo-Trueba and Thomas Drugman and Shira Calamaro and Bozena Kostek},
year = {2021},
booktitle = {{Interspeech 2021}},
... | We propose a weakly-supervised model for word-level mispronunciation
detection in non-native (L2) English speech. To train this model, phonetically
transcribed L2 speech is not required and we only need to mark mispronounced
words. The lack of phonetic transcriptions for L2 speech means that
the model has to learn only... | 2106.03494 | title_snapshot |
kanda21b_interspeech | End-to-End Speaker-Attributed ASR with Transformer | [
"Naoyuki Kanda",
"Guoli Ye",
"Yashesh Gaur",
"Xiaofei Wang",
"Zhong Meng",
"Zhuo Chen",
"Takuya Yoshioka"
] | https://www.isca-archive.org/interspeech_2021/kanda21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/kanda21b_interspeech.pdf | 10.21437/Interspeech.2021-101 | 4413-4417 | @inproceedings{kanda21b_interspeech,
title = {{End-to-End Speaker-Attributed ASR with Transformer}},
author = {Naoyuki Kanda and Guoli Ye and Yashesh Gaur and Xiaofei Wang and Zhong Meng and Zhuo Chen and Takuya Yoshioka},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4413--4417},
... | This paper presents our recent effort on end-to-end speaker-attributed
automatic speech recognition, which jointly performs speaker counting,
speech recognition and speaker identification for monaural multi-talker
audio. Firstly, we thoroughly update the model architecture that was
previously designed based on a long s... | 2104.02128 | title_snapshot |
soltau21_interspeech | Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction | [
"Hagen Soltau",
"Mingqiu Wang",
"Izhak Shafran",
"Laurent El Shafey"
] | https://www.isca-archive.org/interspeech_2021/soltau21_interspeech.html | https://www.isca-archive.org/interspeech_2021/soltau21_interspeech.pdf | 10.21437/Interspeech.2021-691 | 4418-4422 | @inproceedings{soltau21_interspeech,
title = {{Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction}},
author = {Hagen Soltau and Mingqiu Wang and Izhak Shafran and Laurent El Shafey},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4418... | In this paper, we describe novel components for extracting clinically
relevant information from medical conversations which will be available
as Google APIs. We describe a transformer-based Recurrent Neural Network
Transducer (RNN-T) model tailored for long-form audio, which can produce
rich transcriptions including sp... | 2104.02219 | title_snapshot |
vidal21_interspeech | Phone-Level Pronunciation Scoring for Spanish Speakers Learning English Using a GOP-DNN System | [
"Jazmín Vidal",
"Cyntia Bonomi",
"Marcelo Sancinetti",
"Luciana Ferrer"
] | https://www.isca-archive.org/interspeech_2021/vidal21_interspeech.html | https://www.isca-archive.org/interspeech_2021/vidal21_interspeech.pdf | 10.21437/Interspeech.2021-745 | 4423-4427 | @inproceedings{vidal21_interspeech,
title = {{Phone-Level Pronunciation Scoring for Spanish Speakers Learning English Using a GOP-DNN System}},
author = {Jazmín Vidal and Cyntia Bonomi and Marcelo Sancinetti and Luciana Ferrer},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4423--4... | In today’s globalized world being able to communicate in English
is crucial to many people. Computer assisted pronunciation training
(CAPT) systems can help students achieve English proficiency by providing
an accessible way to practice, offering personalized feedback. However,
phone-level pronunciation scoring is stil... | null | null |
xu21k_interspeech | Explore wav2vec 2.0 for Mispronunciation Detection | [
"Xiaoshuo Xu",
"Yueteng Kang",
"Songjun Cao",
"Binghuai Lin",
"Long Ma"
] | https://www.isca-archive.org/interspeech_2021/xu21k_interspeech.html | https://www.isca-archive.org/interspeech_2021/xu21k_interspeech.pdf | 10.21437/Interspeech.2021-777 | 4428-4432 | @inproceedings{xu21k_interspeech,
title = {{Explore wav2vec 2.0 for Mispronunciation Detection}},
author = {Xiaoshuo Xu and Yueteng Kang and Songjun Cao and Binghuai Lin and Long Ma},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4428--4432},
doi = {10.21437/Interspeech.202... | This paper presents an initial attempt to use self-supervised learning
for Mispronunciation Detection. Unlike existing methods that use speech
recognition corpus to train models, we exploit unlabeled data and utilize
a self-supervised learning technique, Wav2vec 2.0, for pretraining.
After the pretraining process, the ... | null | null |
ando21_interspeech | Lexical Density Analysis of Word Productions in Japanese English Using Acoustic Word Embeddings | [
"Shintaro Ando",
"Nobuaki Minematsu",
"Daisuke Saito"
] | https://www.isca-archive.org/interspeech_2021/ando21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ando21_interspeech.pdf | 10.21437/Interspeech.2021-853 | 4433-4437 | @inproceedings{ando21_interspeech,
title = {{Lexical Density Analysis of Word Productions in Japanese English Using Acoustic Word Embeddings}},
author = {Shintaro Ando and Nobuaki Minematsu and Daisuke Saito},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4433--4437},
doi =... | In L2 pronunciation, what kind of phonetic errors are more influential
to intelligibility reduction? Teachers say that learners’ utterances
become unintelligible when words are pronounced with such errors that
make the words misidentified as others. In this paper, we focus on
Japanese English (JE), where the number of ... | null | null |
lin21j_interspeech | Deep Feature Transfer Learning for Automatic Pronunciation Assessment | [
"Binghuai Lin",
"Liyuan Wang"
] | https://www.isca-archive.org/interspeech_2021/lin21j_interspeech.html | https://www.isca-archive.org/interspeech_2021/lin21j_interspeech.pdf | 10.21437/Interspeech.2021-931 | 4438-4442 | @inproceedings{lin21j_interspeech,
title = {{Deep Feature Transfer Learning for Automatic Pronunciation Assessment}},
author = {Binghuai Lin and Liyuan Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4438--4442},
doi = {10.21437/Interspeech.2021-931},
issn = {29... | Automatic pronunciation assessment is commonly developed to evaluate
pronunciation quality of second language (L2) learners. Traditional
methods for automatic pronunciation assessment normally utilize speech
features such as Goodness of pronunciation (GOP), which may not provide
sufficient information for the pronuncia... | null | null |
zhang21fa_interspeech | Multilingual Speech Evaluation: Case Studies on English, Malay and Tamil | [
"Huayun Zhang",
"Ke Shi",
"Nancy F. Chen"
] | https://www.isca-archive.org/interspeech_2021/zhang21fa_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21fa_interspeech.pdf | 10.21437/Interspeech.2021-1258 | 4443-4447 | @inproceedings{zhang21fa_interspeech,
title = {{Multilingual Speech Evaluation: Case Studies on English, Malay and Tamil}},
author = {Huayun Zhang and Ke Shi and Nancy F. Chen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4443--4447},
doi = {10.21437/Interspeech.2021-1258... | Speech evaluation is an essential component in computer-assisted language
learning (CALL). While speech evaluation on English has been popular,
automatic speech scoring on low resource languages remains challenging.
Work in this area has focused on monolingual specific designs and handcrafted
features stemming from res... | 2107.03675 | title_snapshot |
peng21e_interspeech | A Study on Fine-Tuning wav2vec2.0 Model for the Task of Mispronunciation Detection and Diagnosis | [
"Linkai Peng",
"Kaiqi Fu",
"Binghuai Lin",
"Dengfeng Ke",
"Jinsong Zhan"
] | https://www.isca-archive.org/interspeech_2021/peng21e_interspeech.html | https://www.isca-archive.org/interspeech_2021/peng21e_interspeech.pdf | 10.21437/Interspeech.2021-1344 | 4448-4452 | @inproceedings{peng21e_interspeech,
title = {{A Study on Fine-Tuning wav2vec2.0 Model for the Task of Mispronunciation Detection and Diagnosis}},
author = {Linkai Peng and Kaiqi Fu and Binghuai Lin and Dengfeng Ke and Jinsong Zhan},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {444... | Mispronunciation detection and diagnosis (MDD) technology is a key
component of computer-assisted pronunciation training system (CAPT).
The mainstream method is based on deep neural network automatic speech
recognition. Unfortunately, the technique requires massive human-annotated
speech recordings for training. Due to... | null | null |
qiao21b_interspeech | The Impact of ASR on the Automatic Analysis of Linguistic Complexity and Sophistication in Spontaneous L2 Speech | [
"Yu Qiao",
"Wei Zhou",
"Elma Kerz",
"Ralf Schlüter"
] | https://www.isca-archive.org/interspeech_2021/qiao21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/qiao21b_interspeech.pdf | 10.21437/Interspeech.2021-1402 | 4453-4457 | @inproceedings{qiao21b_interspeech,
title = {{The Impact of ASR on the Automatic Analysis of Linguistic Complexity and Sophistication in Spontaneous L2 Speech}},
author = {Yu Qiao and Wei Zhou and Elma Kerz and Ralf Schlüter},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4453--445... | In recent years, automated approaches to assessing linguistic complexity
in second language (L2) writing have made significant progress in gauging
learner performance, predicting human ratings of the quality of learner
productions, and benchmarking L2 development. In contrast, there is
comparatively little work in the ... | 2104.08529 | title_snapshot |
tanaka21c_interspeech | End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning | [
"Tomohiro Tanaka",
"Ryo Masumura",
"Mana Ihori",
"Akihiko Takashima",
"Shota Orihashi",
"Naoki Makishima"
] | https://www.isca-archive.org/interspeech_2021/tanaka21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/tanaka21c_interspeech.pdf | 10.21437/Interspeech.2021-1981 | 4458-4462 | @inproceedings{tanaka21c_interspeech,
title = {{End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning}},
author = {Tomohiro Tanaka and Ryo Masumura and Mana Ihori and Akihiko Takashima and Shota Orihashi and Naoki Makishima},
year = {2021},
booktitle = {{Inte... | We propose a semi-supervised learning method for building end-to-end
rich transcription-style automatic speech recognition (RT-ASR) systems
from small-scale rich transcription-style and large-scale common transcription-style
datasets. In spontaneous speech tasks, various speech phenomena such
as fillers, word fragments... | 2107.05382 | title_snapshot |
cumbal21_interspeech | “You don’t understand me!”: Comparing ASR Results for L1 and L2 Speakers of Swedish | [
"Ronald Cumbal",
"Birger Moell",
"José Lopes",
"Olov Engwall"
] | https://www.isca-archive.org/interspeech_2021/cumbal21_interspeech.html | https://www.isca-archive.org/interspeech_2021/cumbal21_interspeech.pdf | 10.21437/Interspeech.2021-2140 | 4463-4467 | @inproceedings{cumbal21_interspeech,
title = {{“You don’t understand me!”: Comparing ASR Results for L1 and L2 Speakers of Swedish}},
author = {Ronald Cumbal and Birger Moell and José Lopes and Olov Engwall},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4463--4467},
doi = ... | The performance of Automatic Speech Recognition (ASR) systems has constantly
increased in state-of-the-art development. However, performance tends
to decrease considerably in more challenging conditions (e.g., background
noise, multiple speaker social conversations) and with more atypical
speakers (e.g., children, non-... | 2405.13379 | title_snapshot |
zhang21ga_interspeech | NeMo Inverse Text Normalization: From Development to Production | [
"Yang Zhang",
"Evelina Bakhturina",
"Kyle Gorman",
"Boris Ginsburg"
] | https://www.isca-archive.org/interspeech_2021/zhang21ga_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21ga_interspeech.pdf | 10.21437/Interspeech.2021-1571 | 4468-4472 | @inproceedings{zhang21ga_interspeech,
title = {{NeMo Inverse Text Normalization: From Development to Production}},
author = {Yang Zhang and Evelina Bakhturina and Kyle Gorman and Boris Ginsburg},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4468--4472},
doi = {10.21437/Int... | Inverse text normalization (ITN) converts spoken-domain automatic speech
recognition (ASR) output into written-domain text to improve the readability
of the ASR output. Many state-of-the-art ITN systems use hand-written
weighted finite-state transducer (WFST) grammars since this task has
extremely low tolerance to unre... | 2104.05055 | title_snapshot |
naijo21_interspeech | Improvement of Automatic English Pronunciation Assessment with Small Number of Utterances Using Sentence Speakability | [
"Satsuki Naijo",
"Akinori Ito",
"Takashi Nose"
] | https://www.isca-archive.org/interspeech_2021/naijo21_interspeech.html | https://www.isca-archive.org/interspeech_2021/naijo21_interspeech.pdf | 10.21437/Interspeech.2021-1132 | 4473-4477 | @inproceedings{naijo21_interspeech,
title = {{Improvement of Automatic English Pronunciation Assessment with Small Number of Utterances Using Sentence Speakability}},
author = {Satsuki Naijo and Akinori Ito and Takashi Nose},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4473--4477... | The current Computer-Assisted Pronunciation Training (CAPT) system
uses DNN-based speech recognition results to evaluate learner’s
pronunciation with high accuracy when using many utterances for the
evaluation. However, when we use only a few utterances, the accuracy
of the CAPT system deteriorates. One reason for the ... | null | null |
haider21_interspeech | Affect Recognition Through Scalogram and Multi-Resolution Cochleagram Features | [
"Fasih Haider",
"Saturnino Luz"
] | https://www.isca-archive.org/interspeech_2021/haider21_interspeech.html | https://www.isca-archive.org/interspeech_2021/haider21_interspeech.pdf | 10.21437/Interspeech.2021-1761 | 4478-4482 | @inproceedings{haider21_interspeech,
title = {{Affect Recognition Through Scalogram and Multi-Resolution Cochleagram Features}},
author = {Fasih Haider and Saturnino Luz},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4478--4482},
doi = {10.21437/Interspeech.2021-1761},
i... | An approach to the categorization of voice samples according to emotions
expressed by the speaker is proposed which uses Multi-Resolution Cochleagram
(MRCG) and scalogram features in a novel way. Audio recordings from
the EmoDB, EMOVO and Savee Data-sets are employed in training and testing
of predictive models consist... | null | null |
liu21n_interspeech | A Speech Emotion Recognition Framework for Better Discrimination of Confusions | [
"Jiawang Liu",
"Haoxiang Wang"
] | https://www.isca-archive.org/interspeech_2021/liu21n_interspeech.html | https://www.isca-archive.org/interspeech_2021/liu21n_interspeech.pdf | 10.21437/Interspeech.2021-718 | 4483-4487 | @inproceedings{liu21n_interspeech,
title = {{A Speech Emotion Recognition Framework for Better Discrimination of Confusions}},
author = {Jiawang Liu and Haoxiang Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4483--4487},
doi = {10.21437/Interspeech.2021-718},
issn ... | Speech emotion recognition (SER) plays an important role in human-machine
interaction (HMI). Various methods have been proposed for the SER task.
However, a common problem in most of the previous studies is some specific
emotions are grossly misclassified. In this paper, we propose a novel
SER framework aiming at discr... | null | null |
li21p_interspeech | Speech Emotion Recognition via Multi-Level Cross-Modal Distillation | [
"Ruichen Li",
"Jinming Zhao",
"Qin Jin"
] | https://www.isca-archive.org/interspeech_2021/li21p_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21p_interspeech.pdf | 10.21437/Interspeech.2021-785 | 4488-4492 | @inproceedings{li21p_interspeech,
title = {{Speech Emotion Recognition via Multi-Level Cross-Modal Distillation}},
author = {Ruichen Li and Jinming Zhao and Qin Jin},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4488--4492},
doi = {10.21437/Interspeech.2021-785},
issn ... | Speech emotion recognition faces the problem that most of the existing
speech corpora are limited in scale and diversity due to the high annotation
cost and label ambiguity. In this work, we explore the task of learning
robust speech emotion representations based on large unlabeled speech
data. Under a simple assumptio... | null | null |
ito21_interspeech | Audio-Visual Speech Emotion Recognition by Disentangling Emotion and Identity Attributes | [
"Koichiro Ito",
"Takuya Fujioka",
"Qinghua Sun",
"Kenji Nagamatsu"
] | https://www.isca-archive.org/interspeech_2021/ito21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ito21_interspeech.pdf | 10.21437/Interspeech.2021-809 | 4493-4497 | @inproceedings{ito21_interspeech,
title = {{Audio-Visual Speech Emotion Recognition by Disentangling Emotion and Identity Attributes}},
author = {Koichiro Ito and Takuya Fujioka and Qinghua Sun and Kenji Nagamatsu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4493--4497},
doi ... | In this paper, we propose an audio-visual speech emotion recognition
(AV-SER) that can suppress the disturbance from an identity attribute
by disentangling an emotion attribute and an identity one. We developed
a model that first disentangles both attributes for each modality.
In order to achieve the disentanglement, w... | null | null |
bose21_interspeech | Parametric Distributions to Model Numerical Emotion Labels | [
"Deboshree Bose",
"Vidhyasaharan Sethu",
"Eliathamby Ambikairajah"
] | https://www.isca-archive.org/interspeech_2021/bose21_interspeech.html | https://www.isca-archive.org/interspeech_2021/bose21_interspeech.pdf | 10.21437/Interspeech.2021-1000 | 4498-4502 | @inproceedings{bose21_interspeech,
title = {{Parametric Distributions to Model Numerical Emotion Labels}},
author = {Deboshree Bose and Vidhyasaharan Sethu and Eliathamby Ambikairajah},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4498--4502},
doi = {10.21437/Interspeech.2... | It is common to represent emotional states as values on a set of numerical
scales corresponding to attributes such as arousal and valence. Often
these labels are obtained from multiple annotators who record their
perception of emotion in terms of these attributes. Combining these
multiple annotations by taking the mean... | null | null |
gao21e_interspeech | Metric Learning Based Feature Representation with Gated Fusion Model for Speech Emotion Recognition | [
"Yuan Gao",
"Jiaxing Liu",
"Longbiao Wang",
"Jianwu Dang"
] | https://www.isca-archive.org/interspeech_2021/gao21e_interspeech.html | https://www.isca-archive.org/interspeech_2021/gao21e_interspeech.pdf | 10.21437/Interspeech.2021-1133 | 4503-4507 | @inproceedings{gao21e_interspeech,
title = {{Metric Learning Based Feature Representation with Gated Fusion Model for Speech Emotion Recognition}},
author = {Yuan Gao and Jiaxing Liu and Longbiao Wang and Jianwu Dang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4503--4507},
do... | Due to the lack of sufficient speech emotional data, the recognition
performance of existing speech emotion recognition (SER) approaches
is relatively low and requires further improvement to meet the needs
of real-life applications. For the problem of data scarcity, an increasingly
popular solution is to transfer emoti... | null | null |
cai21b_interspeech | Speech Emotion Recognition with Multi-Task Learning | [
"Xingyu Cai",
"Jiahong Yuan",
"Renjie Zheng",
"Liang Huang",
"Kenneth Church"
] | https://www.isca-archive.org/interspeech_2021/cai21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/cai21b_interspeech.pdf | 10.21437/Interspeech.2021-1852 | 4508-4512 | @inproceedings{cai21b_interspeech,
title = {{Speech Emotion Recognition with Multi-Task Learning}},
author = {Xingyu Cai and Jiahong Yuan and Renjie Zheng and Liang Huang and Kenneth Church},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4508--4512},
doi = {10.21437/Intersp... | Speech emotion recognition (SER) classifies speech into emotion categories
such as: Happy, Angry, Sad and Neutral . Recently, deep
learning has been applied to the SER task. This paper proposes a multi-task
learning (MTL) framework to simultaneously perform speech-to-text recognition
and emotion classification, with an... | null | null |
seneviratne21b_interspeech | Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables | [
"Nadee Seneviratne",
"Carol Espy-Wilson"
] | https://www.isca-archive.org/interspeech_2021/seneviratne21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/seneviratne21b_interspeech.pdf | 10.21437/Interspeech.2021-1960 | 4513-4517 | @inproceedings{seneviratne21b_interspeech,
title = {{Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables}},
author = {Nadee Seneviratne and Carol Espy-Wilson},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4513--4517},
doi = {10.2143... | Depression detection using vocal biomarkers is a highly researched
area. Articulatory coordination features (ACFs) are developed based
on the changes in neuromotor coordination due to psychomotor slowing,
a key feature of Major Depressive Disorder. However findings of existing
studies are mostly validated on a single d... | 2011.06739 | title_snapshot |
wang21ga_interspeech | Learning Mutual Correlation in Multimodal Transformer for Speech Emotion Recognition | [
"Yuhua Wang",
"Guang Shen",
"Yuezhu Xu",
"Jiahang Li",
"Zhengdao Zhao"
] | https://www.isca-archive.org/interspeech_2021/wang21ga_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21ga_interspeech.pdf | 10.21437/Interspeech.2021-2004 | 4518-4522 | @inproceedings{wang21ga_interspeech,
title = {{Learning Mutual Correlation in Multimodal Transformer for Speech Emotion Recognition}},
author = {Yuhua Wang and Guang Shen and Yuezhu Xu and Jiahang Li and Zhengdao Zhao},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4518--4522},
d... | Various studies have confirmed the necessity and benefits of leveraging
multimodal features for SER, and the latest research results show that
the temporal information captured by the transformer is very useful
for improving multimodal speech emotion recognition. However, the dependency
between different modalities and... | null | null |
liu21o_interspeech | Time-Frequency Representation Learning with Graph Convolutional Network for Dialogue-Level Speech Emotion Recognition | [
"Jiaxing Liu",
"Yaodong Song",
"Longbiao Wang",
"Jianwu Dang",
"Ruiguo Yu"
] | https://www.isca-archive.org/interspeech_2021/liu21o_interspeech.html | https://www.isca-archive.org/interspeech_2021/liu21o_interspeech.pdf | 10.21437/Interspeech.2021-2067 | 4523-4527 | @inproceedings{liu21o_interspeech,
title = {{Time-Frequency Representation Learning with Graph Convolutional Network for Dialogue-Level Speech Emotion Recognition}},
author = {Jiaxing Liu and Yaodong Song and Longbiao Wang and Jianwu Dang and Ruiguo Yu},
year = {2021},
booktitle = {{Interspeech 2021... | With the development of speech emotion recognition (SER), dialogue-level
SER (DSER) is more aligned with actual scenarios. In this paper, we
propose a DSER approach that includes two stages of representation
learning: intra-utterance representation learning and inter-utterance
representation learning. In the intra-utte... | null | null |
mordido21_interspeech | Compressing 1D Time-Channel Separable Convolutions Using Sparse Random Ternary Matrices | [
"Gonçalo Mordido",
"Matthijs Van keirsbilck",
"Alexander Keller"
] | https://www.isca-archive.org/interspeech_2021/mordido21_interspeech.html | https://www.isca-archive.org/interspeech_2021/mordido21_interspeech.pdf | 10.21437/Interspeech.2021-141 | 4528-4532 | @inproceedings{mordido21_interspeech,
title = {{Compressing 1D Time-Channel Separable Convolutions Using Sparse Random Ternary Matrices}},
author = {Gonçalo Mordido and Matthijs {Van keirsbilck} and Alexander Keller},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4528--4532},
doi... | We demonstrate that 1×1-convolutions in 1D time-channel separable
convolutions may be replaced by constant, sparse random ternary matrices
with weights in -1, 0, +1. Such layers do not perform any multiplications
and do not require training. Moreover, the matrices may be generated
on the chip during computation and the... | 2103.17142 | title_snapshot |
cheng21c_interspeech | Weakly Supervised Construction of ASR Systems from Massive Video Data | [
"Mengli Cheng",
"Chengyu Wang",
"Jun Huang",
"Xiaobo Wang"
] | https://www.isca-archive.org/interspeech_2021/cheng21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/cheng21c_interspeech.pdf | 10.21437/Interspeech.2021-7 | 4533-4537 | @inproceedings{cheng21c_interspeech,
title = {{Weakly Supervised Construction of ASR Systems from Massive Video Data}},
author = {Mengli Cheng and Chengyu Wang and Jun Huang and Xiaobo Wang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4533--4537},
doi = {10.21437/Intersp... | Despite the rapid development of deep learning models, for real-world
applications, building large-scale Automatic Speech Recognition (ASR)
systems from scratch is still significantly challenging, mostly due
to the time-consuming and financially-expensive process of annotating
a large amount of audio data with transcri... | 2008.01300 | title_judge |
kim21l_interspeech | Broadcasted Residual Learning for Efficient Keyword Spotting | [
"Byeonggeun Kim",
"Simyung Chang",
"Jinkyu Lee",
"Dooyong Sung"
] | https://www.isca-archive.org/interspeech_2021/kim21l_interspeech.html | https://www.isca-archive.org/interspeech_2021/kim21l_interspeech.pdf | 10.21437/Interspeech.2021-383 | 4538-4542 | @inproceedings{kim21l_interspeech,
title = {{Broadcasted Residual Learning for Efficient Keyword Spotting}},
author = {Byeonggeun Kim and Simyung Chang and Jinkyu Lee and Dooyong Sung},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {4538--4542},
doi = {10.21437/Interspeech.2... | Keyword spotting is an important research field because it plays a
key role in device wake-up and user interaction on smart devices. However,
it is challenging to minimize errors while operating efficiently in
devices with limited resources such as mobile phones. We present a broadcasted residual learning method to ach... | 2106.04140 | title_snapshot |
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