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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|---|---|---|---|---|---|---|---|---|---|---|
kim21g_interspeech | Multi-Domain Knowledge Distillation via Uncertainty-Matching for End-to-End ASR Models | [
"Ho-Gyeong Kim",
"Min-Joong Lee",
"Hoshik Lee",
"Tae Gyoon Kang",
"Jihyun Lee",
"Eunho Yang",
"Sung Ju Hwang"
] | https://www.isca-archive.org/interspeech_2021/kim21g_interspeech.html | https://www.isca-archive.org/interspeech_2021/kim21g_interspeech.pdf | 10.21437/Interspeech.2021-1169 | 2531-2535 | @inproceedings{kim21g_interspeech,
title = {{Multi-Domain Knowledge Distillation via Uncertainty-Matching for End-to-End ASR Models}},
author = {Ho-Gyeong Kim and Min-Joong Lee and Hoshik Lee and Tae Gyoon Kang and Jihyun Lee and Eunho Yang and Sung Ju Hwang},
year = {2021},
booktitle = {{Interspeec... | Knowledge Distillation basically matches predictive distributions of
student and teacher networks to improve performance in an environment
with model capacity and/or data constraints. However, it is well known
that predictive distribution of neural networks not only tends to be
overly confident, but also cannot directl... | null | null |
macoskey21_interspeech | Learning a Neural Diff for Speech Models | [
"Jonathan Macoskey",
"Grant P. Strimel",
"Ariya Rastrow"
] | https://www.isca-archive.org/interspeech_2021/macoskey21_interspeech.html | https://www.isca-archive.org/interspeech_2021/macoskey21_interspeech.pdf | 10.21437/Interspeech.2021-1575 | 2536-2540 | @inproceedings{macoskey21_interspeech,
title = {{Learning a Neural Diff for Speech Models}},
author = {Jonathan Macoskey and Grant P. Strimel and Ariya Rastrow},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2536--2540},
doi = {10.21437/Interspeech.2021-1575},
issn =... | As more speech processing applications execute locally on edge devices,
a set of resource constraints must be considered. In this work we address
one of these constraints, namely over-the-network data budgets for
transferring models from server to device. We present neural update
approaches for release of subsequent sp... | 2108.01561 | title_snapshot |
zhang21p_interspeech | Stochastic Attention Head Removal: A Simple and Effective Method for Improving Transformer Based ASR Models | [
"Shucong Zhang",
"Erfan Loweimi",
"Peter Bell",
"Steve Renals"
] | https://www.isca-archive.org/interspeech_2021/zhang21p_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21p_interspeech.pdf | 10.21437/Interspeech.2021-280 | 2541-2545 | @inproceedings{zhang21p_interspeech,
title = {{Stochastic Attention Head Removal: A Simple and Effective Method for Improving Transformer Based ASR Models}},
author = {Shucong Zhang and Erfan Loweimi and Peter Bell and Steve Renals},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {25... | Recently, Transformer based models have shown competitive automatic
speech recognition (ASR) performance. One key factor in the success
of these models is the multi-head attention mechanism. However, for
trained models, we have previously observed that many attention matrices
are close to diagonal, indicating the redun... | 2011.04004 | title_snapshot |
xue21b_interspeech | Model-Agnostic Fast Adaptive Multi-Objective Balancing Algorithm for Multilingual Automatic Speech Recognition Model Training | [
"Jiabin Xue",
"Tieran Zheng",
"Jiqing Han"
] | https://www.isca-archive.org/interspeech_2021/xue21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/xue21b_interspeech.pdf | 10.21437/Interspeech.2021-355 | 2546-2550 | @inproceedings{xue21b_interspeech,
title = {{Model-Agnostic Fast Adaptive Multi-Objective Balancing Algorithm for Multilingual Automatic Speech Recognition Model Training}},
author = {Jiabin Xue and Tieran Zheng and Jiqing Han},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2546--2... | This paper regards multilingual automatic speech recognition model
training as a multi-objective problem because learning different languages
may conflict, necessitating a trade-off. Most previous works on multilingual
ASR model training mainly used data sampling to balance the performance
of multiple languages but ign... | null | null |
chang21b_interspeech | Towards Lifelong Learning of End-to-End ASR | [
"Heng-Jui Chang",
"Hung-yi Lee",
"Lin-shan Lee"
] | https://www.isca-archive.org/interspeech_2021/chang21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/chang21b_interspeech.pdf | 10.21437/Interspeech.2021-563 | 2551-2555 | @inproceedings{chang21b_interspeech,
title = {{Towards Lifelong Learning of End-to-End ASR}},
author = {Heng-Jui Chang and Hung-yi Lee and Lin-shan Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2551--2555},
doi = {10.21437/Interspeech.2021-563},
issn = {2958-17... | Automatic speech recognition (ASR) technologies today are primarily
optimized for given datasets; thus, any changes in the application
environment (e.g., acoustic conditions or topic domains) may inevitably
degrade the performance. We can collect new data describing the new
environment and fine-tune the system, but thi... | 2104.01616 | title_snapshot |
leal21_interspeech | Self-Adaptive Distillation for Multilingual Speech Recognition: Leveraging Student Independence | [
"Isabel Leal",
"Neeraj Gaur",
"Parisa Haghani",
"Brian Farris",
"Pedro J. Moreno",
"Manasa Prasad",
"Bhuvana Ramabhadran",
"Yun Zhu"
] | https://www.isca-archive.org/interspeech_2021/leal21_interspeech.html | https://www.isca-archive.org/interspeech_2021/leal21_interspeech.pdf | 10.21437/Interspeech.2021-614 | 2556-2560 | @inproceedings{leal21_interspeech,
title = {{Self-Adaptive Distillation for Multilingual Speech Recognition: Leveraging Student Independence}},
author = {Isabel Leal and Neeraj Gaur and Parisa Haghani and Brian Farris and Pedro J. Moreno and Manasa Prasad and Bhuvana Ramabhadran and Yun Zhu},
year = {... | With a large population of the world speaking more than one language,
multilingual automatic speech recognition (ASR) has gained popularity
in the recent years. While lower resource languages can benefit from
quality improvements in a multilingual ASR system, including unrelated
or higher resource languages in the mix ... | null | null |
xu21f_interspeech | Regularizing Word Segmentation by Creating Misspellings | [
"Hainan Xu",
"Kartik Audhkhasi",
"Yinghui Huang",
"Jesse Emond",
"Bhuvana Ramabhadran"
] | https://www.isca-archive.org/interspeech_2021/xu21f_interspeech.html | https://www.isca-archive.org/interspeech_2021/xu21f_interspeech.pdf | 10.21437/Interspeech.2021-648 | 2561-2565 | @inproceedings{xu21f_interspeech,
title = {{Regularizing Word Segmentation by Creating Misspellings}},
author = {Hainan Xu and Kartik Audhkhasi and Yinghui Huang and Jesse Emond and Bhuvana Ramabhadran},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2561--2565},
doi = {10.2... | This work focuses on improving subword segmentation algorithms for
end-to-end speech recognition models, and makes two major contributions.
Firstly, we propose a novel word segmentation algorithm. The algorithm
uses the same vocabulary generated by a regular wordpiece model, is
easily extensible and supports a variety ... | null | null |
wang21t_interspeech | Multitask Training with Text Data for End-to-End Speech Recognition | [
"Peidong Wang",
"Tara N. Sainath",
"Ron J. Weiss"
] | https://www.isca-archive.org/interspeech_2021/wang21t_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21t_interspeech.pdf | 10.21437/Interspeech.2021-683 | 2566-2570 | @inproceedings{wang21t_interspeech,
title = {{Multitask Training with Text Data for End-to-End Speech Recognition}},
author = {Peidong Wang and Tara N. Sainath and Ron J. Weiss},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2566--2570},
doi = {10.21437/Interspeech.2021-683... | We propose a multitask training method for attention-based end-to-end
speech recognition models. We regularize the decoder in a listen, attend,
and spell model by multitask training it on both audio-text and text-only
data. Trained on the 100-hour subset of LibriSpeech, the proposed method,
without requiring an additio... | 2010.14318 | title_snapshot |
chen21j_interspeech | Emitting Word Timings with HMM-Free End-to-End System in Automatic Speech Recognition | [
"Xianzhao Chen",
"Hao Ni",
"Yi He",
"Kang Wang",
"Zejun Ma",
"Zongxia Xie"
] | https://www.isca-archive.org/interspeech_2021/chen21j_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21j_interspeech.pdf | 10.21437/Interspeech.2021-894 | 2571-2575 | @inproceedings{chen21j_interspeech,
title = {{Emitting Word Timings with HMM-Free End-to-End System in Automatic Speech Recognition}},
author = {Xianzhao Chen and Hao Ni and Yi He and Kang Wang and Zejun Ma and Zongxia Xie},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2571--2575}... | Word timings, which mark the start and end times of each word in ASR
results, play an important part in many applications, such as computer
assisted language learning. To date, end-to-end (E2E) systems outperform
conventional DNN-HMM hybrid systems in ASR accuracy but have challenges
to obtain accurate word timings. In... | null | null |
droppo21_interspeech | Scaling Laws for Acoustic Models | [
"Jasha Droppo",
"Oguz Elibol"
] | https://www.isca-archive.org/interspeech_2021/droppo21_interspeech.html | https://www.isca-archive.org/interspeech_2021/droppo21_interspeech.pdf | 10.21437/Interspeech.2021-1644 | 2576-2580 | @inproceedings{droppo21_interspeech,
title = {{Scaling Laws for Acoustic Models}},
author = {Jasha Droppo and Oguz Elibol},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2576--2580},
doi = {10.21437/Interspeech.2021-1644},
issn = {2958-1796},
} | There is a recent trend in machine learning to increase model quality
by growing models to sizes previously thought to be unreasonable. Recent
work has shown that autoregressive generative models with cross-entropy
objective functions exhibit smooth power-law relationships, or scaling
laws, that predict model quality f... | 2106.09488 | title_snapshot |
billa21_interspeech | Leveraging Non-Target Language Resources to Improve ASR Performance in a Target Language | [
"Jayadev Billa"
] | https://www.isca-archive.org/interspeech_2021/billa21_interspeech.html | https://www.isca-archive.org/interspeech_2021/billa21_interspeech.pdf | 10.21437/Interspeech.2021-1657 | 2581-2585 | @inproceedings{billa21_interspeech,
title = {{Leveraging Non-Target Language Resources to Improve ASR Performance in a Target Language}},
author = {Jayadev Billa},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2581--2585},
doi = {10.21437/Interspeech.2021-1657},
issn ... | This paper investigates approaches to improving automatic speech recognition
(ASR) performance in a target language using resources in other languages.
In particular, we assume that we have untranscribed speech in a different
language and a well trained ASR system in yet another language. Concretely,
we structure this ... | null | null |
fasoli21_interspeech | 4-Bit Quantization of LSTM-Based Speech Recognition Models | [
"Andrea Fasoli",
"Chia-Yu Chen",
"Mauricio Serrano",
"Xiao Sun",
"Naigang Wang",
"Swagath Venkataramani",
"George Saon",
"Xiaodong Cui",
"Brian Kingsbury",
"Wei Zhang",
"Zoltán Tüske",
"Kailash Gopalakrishnan"
] | https://www.isca-archive.org/interspeech_2021/fasoli21_interspeech.html | https://www.isca-archive.org/interspeech_2021/fasoli21_interspeech.pdf | 10.21437/Interspeech.2021-1962 | 2586-2590 | @inproceedings{fasoli21_interspeech,
title = {{4-Bit Quantization of LSTM-Based Speech Recognition Models}},
author = {Andrea Fasoli and Chia-Yu Chen and Mauricio Serrano and Xiao Sun and Naigang Wang and Swagath Venkataramani and George Saon and Xiaodong Cui and Brian Kingsbury and Wei Zhang and Zoltán Tüsk... | We investigate the impact of aggressive low-precision representations
of weights and activations in two families of large LSTM-based architectures
for Automatic Speech Recognition (ASR): hybrid Deep Bidirectional LSTM
- Hidden Markov Models (DBLSTM-HMMs) and Recurrent Neural Network -
Transducers (RNN-Ts). Using a 4-bi... | 2108.12074 | title_snapshot |
masumura21_interspeech | Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation | [
"Ryo Masumura",
"Daiki Okamura",
"Naoki Makishima",
"Mana Ihori",
"Akihiko Takashima",
"Tomohiro Tanaka",
"Shota Orihashi"
] | https://www.isca-archive.org/interspeech_2021/masumura21_interspeech.html | https://www.isca-archive.org/interspeech_2021/masumura21_interspeech.pdf | 10.21437/Interspeech.2021-2043 | 2591-2595 | @inproceedings{masumura21_interspeech,
title = {{Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation}},
author = {Ryo Masumura and Daiki Okamura and Naoki Makishima and Mana Ihori and Akihiko Takashima and Tomohiro Tanaka and Shota ... | In this paper, we present a novel modeling method for single-channel
multi-talker overlapped automatic speech recognition (ASR) systems.
Fully neural network based end-to-end models have dramatically improved
the performance of multi-taker overlapped ASR tasks. One promising
approach for end-to-end modeling is autoregr... | 2107.01549 | title_snapshot |
meng21_interspeech | Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition | [
"Zhong Meng",
"Yu Wu",
"Naoyuki Kanda",
"Liang Lu",
"Xie Chen",
"Guoli Ye",
"Eric Sun",
"Jinyu Li",
"Yifan Gong"
] | https://www.isca-archive.org/interspeech_2021/meng21_interspeech.html | https://www.isca-archive.org/interspeech_2021/meng21_interspeech.pdf | 10.21437/Interspeech.2021-2075 | 2596-2600 | @inproceedings{meng21_interspeech,
title = {{Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition}},
author = {Zhong Meng and Yu Wu and Naoyuki Kanda and Liang Lu and Xie Chen and Guoli Ye and Eric Sun and Jinyu Li and Yifan Gong},
year = {2021},
booktitle = ... | Integrating external language models (LMs) into end-to-end (E2E) models
remains a challenging task for domain-adaptive speech recognition.
Recently, internal language model estimation (ILME)-based LM fusion
has shown significant word error rate (WER) reduction from Shallow
Fusion by subtracting a weighted internal LM s... | 2106.02302 | title_snapshot |
jiang21b_interspeech | Variable Frame Rate Acoustic Models Using Minimum Error Reinforcement Learning | [
"Dongcheng Jiang",
"Chao Zhang",
"Philip C. Woodland"
] | https://www.isca-archive.org/interspeech_2021/jiang21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/jiang21b_interspeech.pdf | 10.21437/Interspeech.2021-2198 | 2601-2605 | @inproceedings{jiang21b_interspeech,
title = {{Variable Frame Rate Acoustic Models Using Minimum Error Reinforcement Learning}},
author = {Dongcheng Jiang and Chao Zhang and Philip C. Woodland},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2601--2605},
doi = {10.21437/Inte... | Frame selection in automatic speech recognition (ASR) systems can potentially
improve the trade-off between speed and accuracy relative to fixed
low frame rate methods. In this paper, a sequence training approach
based on minimum error and reinforcement learning is proposed for a
hybrid ASR system to operate at a varia... | null | null |
kaland21_interspeech | How f0 and Phrase Position Affect Papuan Malay Word Identification | [
"Constantijn Kaland",
"Matthew Gordon"
] | https://www.isca-archive.org/interspeech_2021/kaland21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kaland21_interspeech.pdf | 10.21437/Interspeech.2021-6 | 2606-2610 | @inproceedings{kaland21_interspeech,
title = {{How f0 and Phrase Position Affect Papuan Malay Word Identification}},
author = {Constantijn Kaland and Matthew Gordon},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2606--2610},
doi = {10.21437/Interspeech.2021-6},
issn ... | This paper reports a perception experiment on Papuan Malay, an Eastern
Indonesian language for which phrase prosody is largely underresearched.
While phrase-final f0 movements are the most prominent ones in this
language, it remains to be seen to what extent they signal phrase boundaries
(demarcating) or whether they c... | null | null |
jespersen21_interspeech | On the Feasibility of the Danish Model of Intonational Transcription: Phonetic Evidence from Jutlandic Danish | [
"Anna Bothe Jespersen",
"Pavel Šturm",
"Míša Hejná"
] | https://www.isca-archive.org/interspeech_2021/jespersen21_interspeech.html | https://www.isca-archive.org/interspeech_2021/jespersen21_interspeech.pdf | 10.21437/Interspeech.2021-190 | 2611-2615 | @inproceedings{jespersen21_interspeech,
title = {{On the Feasibility of the Danish Model of Intonational Transcription: Phonetic Evidence from Jutlandic Danish}},
author = {Anna Bothe Jespersen and Pavel Šturm and Míša Hejná},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2611--261... | Most of our knowledge of Danish f0 variation and intonation is based
on the work of Grønnum and colleagues, who developed an a-phonological
model in which a series of repeated “default” contours
are superpositioned onto an overarching f0 slope. The current paper
tests a range of predictions stemming from this model, mo... | null | null |
meli21_interspeech | An Experiment in Paratone Detection in a Prosodically Annotated EAP Spoken Corpus | [
"Adrien Méli",
"Nicolas Ballier",
"Achille Falaise",
"Alice Henderson"
] | https://www.isca-archive.org/interspeech_2021/meli21_interspeech.html | https://www.isca-archive.org/interspeech_2021/meli21_interspeech.pdf | 10.21437/Interspeech.2021-294 | 2616-2620 | @inproceedings{meli21_interspeech,
title = {{An Experiment in Paratone Detection in a Prosodically Annotated EAP Spoken Corpus}},
author = {Adrien Méli and Nicolas Ballier and Achille Falaise and Alice Henderson},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2616--2620},
doi ... | This article describes an experiment in paratone detection based on
a spoken corpus of English for Academic Purposes (EAP) recently automatically
re-annotated with prosodic information. The Momel and INTSINT annotations
were carried out using SPPAS. The EIIDA corpus was chosen as it offered
long uninterrupted stretches... | null | null |
gerazov21_interspeech | ProsoBeast Prosody Annotation Tool | [
"Branislav Gerazov",
"Michael Wagner"
] | https://www.isca-archive.org/interspeech_2021/gerazov21_interspeech.html | https://www.isca-archive.org/interspeech_2021/gerazov21_interspeech.pdf | 10.21437/Interspeech.2021-304 | 2621-2625 | @inproceedings{gerazov21_interspeech,
title = {{ProsoBeast Prosody Annotation Tool}},
author = {Branislav Gerazov and Michael Wagner},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2621--2625},
doi = {10.21437/Interspeech.2021-304},
issn = {2958-1796},
} | The labelling of speech corpora is a laborious and time-consuming process.
The ProsoBeast Annotation Tool seeks to ease and accelerate this process
by providing an interactive 2D representation of the prosodic landscape
of the data, in which contours are distributed based on their similarity.
This interactive map allow... | 2104.02397 | title_snapshot |
tran21_interspeech | Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts | [
"Trang Tran",
"Mari Ostendorf"
] | https://www.isca-archive.org/interspeech_2021/tran21_interspeech.html | https://www.isca-archive.org/interspeech_2021/tran21_interspeech.pdf | 10.21437/Interspeech.2021-373 | 2626-2630 | @inproceedings{tran21_interspeech,
title = {{Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts}},
author = {Trang Tran and Mari Ostendorf},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2626--2630},
doi = {10.21437/Interspeech.2021-373},
issn ... | This work explores constituency parsing on automatically recognized
transcripts of conversational speech. The neural parser is based on
a sentence encoder that leverages word vectors contextualized with
prosodic features, jointly learning prosodic feature extraction with
parsing. We assess the utility of the prosody in... | 2106.07794 | title_snapshot |
liu21i_interspeech | Targeted and Targetless Neutral Tones in Taiwanese Southern Min | [
"Roger Cheng-yen Liu",
"Feng-fan Hsieh",
"Yueh-chin Chang"
] | https://www.isca-archive.org/interspeech_2021/liu21i_interspeech.html | https://www.isca-archive.org/interspeech_2021/liu21i_interspeech.pdf | 10.21437/Interspeech.2021-434 | 2631-2635 | @inproceedings{liu21i_interspeech,
title = {{Targeted and Targetless Neutral Tones in Taiwanese Southern Min}},
author = {Roger Cheng-yen Liu and Feng-fan Hsieh and Yueh-chin Chang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2631--2635},
doi = {10.21437/Interspeech.2021... | This article is an acoustic study on the two types of neutral tone
in Taiwanese Southern Min (TSM). Recording materials included a set
of verb-clitic constructions with different preceding tones and clitics.
Pitch contours in different conditions were compared using Smoothing
Spline ANOVA. Our results confirmed that Ty... | null | null |
gosy21_interspeech | The Interaction of Word Complexity and Word Duration in an Agglutinative Language | [
"Mária Gósy",
"Kálmán Abari"
] | https://www.isca-archive.org/interspeech_2021/gosy21_interspeech.html | https://www.isca-archive.org/interspeech_2021/gosy21_interspeech.pdf | 10.21437/Interspeech.2021-594 | 2636-2640 | @inproceedings{gosy21_interspeech,
title = {{The Interaction of Word Complexity and Word Duration in an Agglutinative Language}},
author = {Mária Gósy and Kálmán Abari},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2636--2640},
doi = {10.21437/Interspeech.2021-594},
issn... | The mental lexicon comprises the representations of various words either
in a morphologically decomposed form, or in a conceptually non-decomposed
form. The durations of mono-morphemic and multimorphemic words are
assumed to contain information on the routes of their lexical access. The durations of Hungarian nouns wit... | null | null |
pan21b_interspeech | Taiwan Min Nan (Taiwanese) Checked Tones Sound Change | [
"Ho-hsien Pan",
"Shao-ren Lyu"
] | https://www.isca-archive.org/interspeech_2021/pan21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/pan21b_interspeech.pdf | 10.21437/Interspeech.2021-672 | 2641-2645 | @inproceedings{pan21b_interspeech,
title = {{Taiwan Min Nan (Taiwanese) Checked Tones Sound Change}},
author = {Ho-hsien Pan and Shao-ren Lyu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2641--2645},
doi = {10.21437/Interspeech.2021-672},
issn = {2958-1796},
} | The multifaced changes of Taiwan Min Nan (TMN) checked sandhi tones,
S3 and S5 were investigated as well as the checked base tones, B3 and
B5. Simultaneous EGG data, CQ_H and acoustic data, including duration,
f0 offset at 80% vowel interval, and spectral tilt H1 * -A3 * from forty male and female speakers above 40 and... | null | null |
jakob21_interspeech | In-Group Advantage in the Perception of Emotions: Evidence from Three Varieties of German | [
"Moritz Jakob",
"Bettina Braun",
"Katharina Zahner-Ritter"
] | https://www.isca-archive.org/interspeech_2021/jakob21_interspeech.html | https://www.isca-archive.org/interspeech_2021/jakob21_interspeech.pdf | 10.21437/Interspeech.2021-1172 | 2646-2650 | @inproceedings{jakob21_interspeech,
title = {{In-Group Advantage in the Perception of Emotions: Evidence from Three Varieties of German}},
author = {Moritz Jakob and Bettina Braun and Katharina Zahner-Ritter},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2646--2650},
doi =... | Various studies on the perception of vocally expressed emotions have
shown that recognition rates are higher if speaker and listener belong
to the same cultural or linguistic group. This so-called in-group
advantage is commonly attributed to prosodic differences in the
expression of emotion across groups. Evidence come... | null | null |
gobl21_interspeech | The LF Model in the Frequency Domain for Glottal Airflow Modelling Without Aliasing Distortion | [
"Christer Gobl"
] | https://www.isca-archive.org/interspeech_2021/gobl21_interspeech.html | https://www.isca-archive.org/interspeech_2021/gobl21_interspeech.pdf | 10.21437/Interspeech.2021-1625 | 2651-2655 | @inproceedings{gobl21_interspeech,
title = {{The LF Model in the Frequency Domain for Glottal Airflow Modelling Without Aliasing Distortion}},
author = {Christer Gobl},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2651--2655},
doi = {10.21437/Interspeech.2021-1625},
issn... | Many of the commonly used voice source models are based on piecewise
elementary functions defined in the time domain. The discrete-time
implementation of such models generally causes aliasing distortion,
which make them less useful for certain applications. This paper presents
a method which eliminates this distortion.... | null | null |
wagner21_interspeech | Parsing Speech for Grouping and Prominence, and the Typology of Rhythm | [
"Michael Wagner",
"Alvaro Iturralde Zurita",
"Sijia Zhang"
] | https://www.isca-archive.org/interspeech_2021/wagner21_interspeech.html | https://www.isca-archive.org/interspeech_2021/wagner21_interspeech.pdf | 10.21437/Interspeech.2021-1684 | 2656-2660 | @inproceedings{wagner21_interspeech,
title = {{Parsing Speech for Grouping and Prominence, and the Typology of Rhythm}},
author = {Michael Wagner and Alvaro Iturralde Zurita and Sijia Zhang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2656--2660},
doi = {10.21437/Intersp... | Humans appear to be wired to perceive acoustic events rhythmically.
English speakers, for example, tend to perceive alternating short and
long sounds as a series of binary groups with a final beat (iambs),
and alternating soft and loud sounds as a series of trochees. This
generalization, often called the ‘Iambic-trocha... | null | null |
mumtaz21_interspeech | Prosody of Case Markers in Urdu | [
"Benazir Mumtaz",
"Massimiliano Canzi",
"Miriam Butt"
] | https://www.isca-archive.org/interspeech_2021/mumtaz21_interspeech.html | https://www.isca-archive.org/interspeech_2021/mumtaz21_interspeech.pdf | 10.21437/Interspeech.2021-1776 | 2661-2665 | @inproceedings{mumtaz21_interspeech,
title = {{Prosody of Case Markers in Urdu}},
author = {Benazir Mumtaz and Massimiliano Canzi and Miriam Butt},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2661--2665},
doi = {10.21437/Interspeech.2021-1776},
issn = {2958-1796},
... | This paper studies the prosody of case clitics in Urdu, for which various
different claims exist in the literature. We conducted a production
experiment and controlled for effects potentially arising from the
phonetics of the case clitics, the syntactic function they express
and clausal position. We find that case clit... | null | null |
stefansdottir21_interspeech | Articulatory Characteristics of Icelandic Voiced Fricative Lenition: Gradience, Categoricity, and Speaker/Gesture-Specific Effects | [
"Brynhildur Stefansdottir",
"Francesco Burroni",
"Sam Tilsen"
] | https://www.isca-archive.org/interspeech_2021/stefansdottir21_interspeech.html | https://www.isca-archive.org/interspeech_2021/stefansdottir21_interspeech.pdf | 10.21437/Interspeech.2021-1903 | 2666-2670 | @inproceedings{stefansdottir21_interspeech,
title = {{Articulatory Characteristics of Icelandic Voiced Fricative Lenition: Gradience, Categoricity, and Speaker/Gesture-Specific Effects}},
author = {Brynhildur Stefansdottir and Francesco Burroni and Sam Tilsen},
year = {2021},
booktitle = {{Interspee... | Icelandic voiced fricatives frequently reduce in connected speech.
However, systematic investigations of the phenomenon from acoustic
and articulatory perspectives are lacking. To further the understanding
of this lenition process, we present electromagnetic articulography
and acoustic data from four speakers concernin... | null | null |
johnson21_interspeech | Leveraging the Uniformity Framework to Examine Crosslinguistic Similarity for Long-Lag Stops in Spontaneous Cantonese-English Bilingual Speech | [
"Khia A. Johnson"
] | https://www.isca-archive.org/interspeech_2021/johnson21_interspeech.html | https://www.isca-archive.org/interspeech_2021/johnson21_interspeech.pdf | 10.21437/Interspeech.2021-1780 | 2671-2675 | @inproceedings{johnson21_interspeech,
title = {{Leveraging the Uniformity Framework to Examine Crosslinguistic Similarity for Long-Lag Stops in Spontaneous Cantonese-English Bilingual Speech}},
author = {Khia A. Johnson},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2671--2675},
... | While crosslinguistic influence is widespread in bilingual speech production,
it is less clear which aspects of representation are shared across
languages, if any. Most prior work examines phonetically distinct yet
phonologically similar sounds, for which phonetic convergence suggests
a cross-language link within indiv... | null | null |
sivaraman21_interspeech | Personalized Speech Enhancement Through Self-Supervised Data Augmentation and Purification | [
"Aswin Sivaraman",
"Sunwoo Kim",
"Minje Kim"
] | https://www.isca-archive.org/interspeech_2021/sivaraman21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sivaraman21_interspeech.pdf | 10.21437/Interspeech.2021-1868 | 2676-2680 | @inproceedings{sivaraman21_interspeech,
title = {{Personalized Speech Enhancement Through Self-Supervised Data Augmentation and Purification}},
author = {Aswin Sivaraman and Sunwoo Kim and Minje Kim},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2676--2680},
doi = {10.2143... | Training personalized speech enhancement models is innately a no-shot
learning problem due to privacy constraints and limited access to noise-free
speech from the target user. If there is an abundance of unlabeled
noisy speech from the test-time user, one may train a personalized
speech enhancement model using self-sup... | 2104.02018 | title_snapshot |
saddler21_interspeech | Speech Denoising with Auditory Models | [
"Mark R. Saddler",
"Andrew Francl",
"Jenelle Feather",
"Kaizhi Qian",
"Yang Zhang",
"Josh H. McDermott"
] | https://www.isca-archive.org/interspeech_2021/saddler21_interspeech.html | https://www.isca-archive.org/interspeech_2021/saddler21_interspeech.pdf | 10.21437/Interspeech.2021-1973 | 2681-2685 | @inproceedings{saddler21_interspeech,
title = {{Speech Denoising with Auditory Models}},
author = {Mark R. Saddler and Andrew Francl and Jenelle Feather and Kaizhi Qian and Yang Zhang and Josh H. McDermott},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2681--2685},
doi = {... | Contemporary speech enhancement predominantly relies on audio transforms
that are trained to reconstruct a clean speech waveform. The development
of high-performing neural network sound recognition systems has raised
the possibility of using deep feature representations as ‘perceptual’
losses with which to train denois... | 2011.10706 | title_snapshot |
eskimez21b_interspeech | Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement | [
"Sefik Emre Eskimez",
"Xiaofei Wang",
"Min Tang",
"Hemin Yang",
"Zirun Zhu",
"Zhuo Chen",
"Huaming Wang",
"Takuya Yoshioka"
] | https://www.isca-archive.org/interspeech_2021/eskimez21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/eskimez21b_interspeech.pdf | 10.21437/Interspeech.2021-220 | 2686-2690 | @inproceedings{eskimez21b_interspeech,
title = {{Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement}},
author = {Sefik Emre Eskimez and Xiaofei Wang and Min Tang and Hemin Yang and Zirun Zhu and Zhuo Chen and Huaming Wang and Takuya Yoshioka},
year = {2021},
booktitle = ... | With the surge of online meetings, it has become more critical than
ever to provide high-quality speech audio and live captioning under
various noise conditions. However, most monaural speech enhancement
(SE) models introduce processing artifacts and thus degrade the performance
of downstream tasks, including automatic... | 2106.02896 | title_snapshot |
xu21g_interspeech | Multi-Stage Progressive Speech Enhancement Network | [
"Xinmeng Xu",
"Yang Wang",
"Dongxiang Xu",
"Yiyuan Peng",
"Cong Zhang",
"Jie Jia",
"Binbin Chen"
] | https://www.isca-archive.org/interspeech_2021/xu21g_interspeech.html | https://www.isca-archive.org/interspeech_2021/xu21g_interspeech.pdf | 10.21437/Interspeech.2021-520 | 2691-2695 | @inproceedings{xu21g_interspeech,
title = {{Multi-Stage Progressive Speech Enhancement Network}},
author = {Xinmeng Xu and Yang Wang and Dongxiang Xu and Yiyuan Peng and Cong Zhang and Jie Jia and Binbin Chen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2691--2695},
doi ... | Speech enhancement is a fundamental way to separate and generate clean
speech from adverse environment where the received speech is seriously
corrupted by noise. This paper applies a novel progressive network
for speech enhancement by using multi-stage structure, where each stage
contains a channel attention block foll... | null | null |
chang21c_interspeech | Single-Channel Speech Enhancement Using Learnable Loss Mixup | [
"Oscar Chang",
"Dung N. Tran",
"Kazuhito Koishida"
] | https://www.isca-archive.org/interspeech_2021/chang21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/chang21c_interspeech.pdf | 10.21437/Interspeech.2021-859 | 2696-2700 | @inproceedings{chang21c_interspeech,
title = {{Single-Channel Speech Enhancement Using Learnable Loss Mixup}},
author = {Oscar Chang and Dung N. Tran and Kazuhito Koishida},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2696--2700},
doi = {10.21437/Interspeech.2021-859},
... | Generalization remains a major problem in supervised learning of single-channel
speech enhancement. In this work, we propose learnable loss mixup
(LLM) , a simple and effortless training diagram, to improve the
generalization of deep learning-based speech enhancement models. Loss
mixup , of which learnable loss mixup i... | 2312.17255 | title_snapshot |
zhang21q_interspeech | A Maximum Likelihood Approach to SNR-Progressive Learning Using Generalized Gaussian Distribution for LSTM-Based Speech Enhancement | [
"Xiao-Qi Zhang",
"Jun Du",
"Li Chai",
"Chin-Hui Lee"
] | https://www.isca-archive.org/interspeech_2021/zhang21q_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21q_interspeech.pdf | 10.21437/Interspeech.2021-922 | 2701-2705 | @inproceedings{zhang21q_interspeech,
title = {{A Maximum Likelihood Approach to SNR-Progressive Learning Using Generalized Gaussian Distribution for LSTM-Based Speech Enhancement}},
author = {Xiao-Qi Zhang and Jun Du and Li Chai and Chin-Hui Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
p... | A maximum likelihood (ML) approach to characterizing regression errors
in each target layer of SNR progressive learning (PL) using long short-term
memory (LSTM) networks is proposed to improve performances of speech
enhancement at low SNR levels. Each LSTM layer is guided to learn an
intermediate target with a specific... | null | null |
agrawal21_interspeech | Whisper Speech Enhancement Using Joint Variational Autoencoder for Improved Speech Recognition | [
"Vikas Agrawal",
"Shashi Kumar",
"Shakti P. Rath"
] | https://www.isca-archive.org/interspeech_2021/agrawal21_interspeech.html | https://www.isca-archive.org/interspeech_2021/agrawal21_interspeech.pdf | 10.21437/Interspeech.2021-953 | 2706-2710 | @inproceedings{agrawal21_interspeech,
title = {{Whisper Speech Enhancement Using Joint Variational Autoencoder for Improved Speech Recognition}},
author = {Vikas Agrawal and Shashi Kumar and Shakti P. Rath},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2706--2710},
doi = {... | Whispering is the natural choice of communication when one wants to
interact quietly and privately. Due to vast differences in acoustic
characteristics of whisper and natural speech, there is drastic degradation
in the performance of whisper speech when decoded by the Automatic
Speech Recognition (ASR) system trained o... | null | null |
lee21d_interspeech | DEMUCS-Mobile : On-Device Lightweight Speech Enhancement | [
"Lukas Lee",
"Youna Ji",
"Minjae Lee",
"Min-Seok Choi"
] | https://www.isca-archive.org/interspeech_2021/lee21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/lee21d_interspeech.pdf | 10.21437/Interspeech.2021-1025 | 2711-2715 | @inproceedings{lee21d_interspeech,
title = {{DEMUCS-Mobile : On-Device Lightweight Speech Enhancement}},
author = {Lukas Lee and Youna Ji and Minjae Lee and Min-Seok Choi},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2711--2715},
doi = {10.21437/Interspeech.2021-1025},
... | As the importance of speech enhancement for real-world application
increases, the compactness of the model is also becoming a crucial
study. In this paper, we present compression techniques to reduce the
model size and applied them to the state-of-the-art real-time speech
enhancement system. We successfully reduce the ... | null | null |
kashyap21_interspeech | Speech Denoising Without Clean Training Data: A Noise2Noise Approach | [
"Madhav Mahesh Kashyap",
"Anuj Tambwekar",
"Krishnamoorthy Manohara",
"S. Natarajan"
] | https://www.isca-archive.org/interspeech_2021/kashyap21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kashyap21_interspeech.pdf | 10.21437/Interspeech.2021-1130 | 2716-2720 | @inproceedings{kashyap21_interspeech,
title = {{Speech Denoising Without Clean Training Data: A Noise2Noise Approach}},
author = {Madhav Mahesh Kashyap and Anuj Tambwekar and Krishnamoorthy Manohara and S. Natarajan},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2716--2720},
doi... | This paper tackles the problem of the heavy dependence of clean speech
data required by deep learning based audio-denoising methods by showing
that it is possible to train deep speech denoising networks using only
noisy speech samples. Conventional wisdom dictates that in order to
achieve good speech denoising performa... | 2104.03838 | title_snapshot |
dang21_interspeech | Improved Speech Enhancement Using a Complex-Domain GAN with Fused Time-Domain and Time-Frequency Domain Constraints | [
"Feng Dang",
"Pengyuan Zhang",
"Hangting Chen"
] | https://www.isca-archive.org/interspeech_2021/dang21_interspeech.html | https://www.isca-archive.org/interspeech_2021/dang21_interspeech.pdf | 10.21437/Interspeech.2021-1134 | 2721-2725 | @inproceedings{dang21_interspeech,
title = {{Improved Speech Enhancement Using a Complex-Domain GAN with Fused Time-Domain and Time-Frequency Domain Constraints}},
author = {Feng Dang and Pengyuan Zhang and Hangting Chen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2721--2725},
... | Complex-domain models have achieved promising results for speech enhancement
(SE) tasks. Some complex-domain models consider only time-frequency
(T-F) domain constraints and do not take advantage of the information
at the time-domain waveform level. Some complex-domain models consider
only time-domain constraints and d... | null | null |
zhang21r_interspeech | Speech Enhancement with Topology-Enhanced Generative Adversarial Networks (GANs) | [
"Xudong Zhang",
"Liang Zhao",
"Feng Gu"
] | https://www.isca-archive.org/interspeech_2021/zhang21r_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21r_interspeech.pdf | 10.21437/Interspeech.2021-1411 | 2726-2730 | @inproceedings{zhang21r_interspeech,
title = {{Speech Enhancement with Topology-Enhanced Generative Adversarial Networks (GANs)}},
author = {Xudong Zhang and Liang Zhao and Feng Gu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2726--2730},
doi = {10.21437/Interspeech.2021... | Speech enhancement is one of the effective approaches in improving
speech quality. Neural network models have been widely used in speech
enhancement, such as recurrent neural networks (RNNs), long short-term
memory networks (LSTMs), and generative adversarial networks (GANs).
However, some of them either handle the spe... | null | null |
bu21_interspeech | Learning Speech Structure to Improve Time-Frequency Masks | [
"Suliang Bu",
"Yunxin Zhao",
"Shaojun Wang",
"Mei Han"
] | https://www.isca-archive.org/interspeech_2021/bu21_interspeech.html | https://www.isca-archive.org/interspeech_2021/bu21_interspeech.pdf | 10.21437/Interspeech.2021-1859 | 2731-2735 | @inproceedings{bu21_interspeech,
title = {{Learning Speech Structure to Improve Time-Frequency Masks}},
author = {Suliang Bu and Yunxin Zhao and Shaojun Wang and Mei Han},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2731--2735},
doi = {10.21437/Interspeech.2021-1859},
i... | Time-frequency (TF) masks are widely used in speech enhancement (SE).
However, accurately estimating TF masks from noisy speech remains a
challenge to both statistical or neural network approaches. Statistical
model-based mask estimation usually depends on a good parameter initialization,
while NN-based mask estimation... | null | null |
kim21h_interspeech | SE-Conformer: Time-Domain Speech Enhancement Using Conformer | [
"Eesung Kim",
"Hyeji Seo"
] | https://www.isca-archive.org/interspeech_2021/kim21h_interspeech.html | https://www.isca-archive.org/interspeech_2021/kim21h_interspeech.pdf | 10.21437/Interspeech.2021-2207 | 2736-2740 | @inproceedings{kim21h_interspeech,
title = {{SE-Conformer: Time-Domain Speech Enhancement Using Conformer}},
author = {Eesung Kim and Hyeji Seo},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2736--2740},
doi = {10.21437/Interspeech.2021-2207},
issn = {2958-1796},
} | Convolution-augmented transformer (conformer) has recently shown competitive
results in speech-domain applications, such as automatic speech recognition,
continuous speech separation, and sound event detection. Conformer
can capture both the short and long-term temporal sequence information
by attending to the whole se... | null | null |
kongthaworn21_interspeech | Spectral and Latent Speech Representation Distortion for TTS Evaluation | [
"Thananchai Kongthaworn",
"Burin Naowarat",
"Ekapol Chuangsuwanich"
] | https://www.isca-archive.org/interspeech_2021/kongthaworn21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kongthaworn21_interspeech.pdf | 10.21437/Interspeech.2021-2258 | 2741-2745 | @inproceedings{kongthaworn21_interspeech,
title = {{Spectral and Latent Speech Representation Distortion for TTS Evaluation}},
author = {Thananchai Kongthaworn and Burin Naowarat and Ekapol Chuangsuwanich},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2741--2745},
doi = {1... | One of the main problems in the development of text-to-speech (TTS)
systems is its reliance on subjective measures, typically the Mean
Opinion Score (MOS). MOS requires a large number of people to reliably
rate each utterance, making the development process slow and expensive.
Recent research on speech quality assessme... | null | null |
valentinibotinhao21_interspeech | Detection and Analysis of Attention Errors in Sequence-to-Sequence Text-to-Speech | [
"Cassia Valentini-Botinhao",
"Simon King"
] | https://www.isca-archive.org/interspeech_2021/valentinibotinhao21_interspeech.html | https://www.isca-archive.org/interspeech_2021/valentinibotinhao21_interspeech.pdf | 10.21437/Interspeech.2021-286 | 2746-2750 | @inproceedings{valentinibotinhao21_interspeech,
title = {{Detection and Analysis of Attention Errors in Sequence-to-Sequence Text-to-Speech}},
author = {Cassia Valentini-Botinhao and Simon King},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2746--2750},
doi = {10.21437/Int... | Sequence-to-sequence speech synthesis models are notorious for gross
errors such as skipping and repetition, commonly associated with failures
in the attention mechanism. While a lot has been done to improve attention
and decrease errors, this paper focuses instead on automatic error
detection and analysis. We evaluate... | null | null |
zandie21_interspeech | RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis | [
"Rohola Zandie",
"Mohammad H. Mahoor",
"Julia Madsen",
"Eshrat S. Emamian"
] | https://www.isca-archive.org/interspeech_2021/zandie21_interspeech.html | https://www.isca-archive.org/interspeech_2021/zandie21_interspeech.pdf | 10.21437/Interspeech.2021-341 | 2751-2755 | @inproceedings{zandie21_interspeech,
title = {{RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis}},
author = {Rohola Zandie and Mohammad H. Mahoor and Julia Madsen and Eshrat S. Emamian},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2751--2755},
doi = {10.21... | This paper introduces RyanSpeech , a new speech corpus for research
on automated text-to-speech (TTS) systems. Publicly available TTS corpora
are often noisy, recorded with multiple speakers, or lack quality male
speech data. In order to meet the need for a high quality, publicly
available male speech corpus within the... | 2106.08468 | title_snapshot |
shi21c_interspeech | AISHELL-3: A Multi-Speaker Mandarin TTS Corpus | [
"Yao Shi",
"Hui Bu",
"Xin Xu",
"Shaoji Zhang",
"Ming Li"
] | https://www.isca-archive.org/interspeech_2021/shi21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/shi21c_interspeech.pdf | 10.21437/Interspeech.2021-755 | 2756-2760 | @inproceedings{shi21c_interspeech,
title = {{AISHELL-3: A Multi-Speaker Mandarin TTS Corpus}},
author = {Yao Shi and Hui Bu and Xin Xu and Shaoji Zhang and Ming Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2756--2760},
doi = {10.21437/Interspeech.2021-755},
issn ... | In this paper, we present AISHELL-3, a large-scale multi-speaker Mandarin
speech corpus which could be used to train multi-speaker Text-To-Speech
(TTS) systems. The corpus contains roughly 85 hours of emotion-neutral
recordings spanning across 218 native Chinese mandarin speakers. Their
auxiliary attributes such as gen... | 2010.11567 | title_judge |
eng21_interspeech | Comparing Speech Enhancement Techniques for Voice Adaptation-Based Speech Synthesis | [
"Nicholas Eng",
"C.T. Justine Hui",
"Yusuke Hioka",
"Catherine I. Watson"
] | https://www.isca-archive.org/interspeech_2021/eng21_interspeech.html | https://www.isca-archive.org/interspeech_2021/eng21_interspeech.pdf | 10.21437/Interspeech.2021-800 | 2761-2765 | @inproceedings{eng21_interspeech,
title = {{Comparing Speech Enhancement Techniques for Voice Adaptation-Based Speech Synthesis}},
author = {Nicholas Eng and C.T. Justine Hui and Yusuke Hioka and Catherine I. Watson},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2761--2765},
doi... | This study investigates the use of speech enhancement techniques in
creating text-to-speech voices with degraded or noisy speech. A number
of synthetic voices were created using speech that was first degraded
by different noise types at various signal-to-noise ratios (SNRs),
then enhanced through four speech enhancemen... | null | null |
cui21c_interspeech | EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model | [
"Chenye Cui",
"Yi Ren",
"Jinglin Liu",
"Feiyang Chen",
"Rongjie Huang",
"Ming Lei",
"Zhou Zhao"
] | https://www.isca-archive.org/interspeech_2021/cui21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/cui21c_interspeech.pdf | 10.21437/Interspeech.2021-1148 | 2766-2770 | @inproceedings{cui21c_interspeech,
title = {{EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model}},
author = {Chenye Cui and Yi Ren and Jinglin Liu and Feiyang Chen and Rongjie Huang and Ming Lei and Zhou Zhao},
year = {2021},
booktitle = {{Interspeech 2021}},
pa... | Recently, there has been an increasing interest in neural speech synthesis.
While the deep neural network achieves the state-of-the-art result
in text-to-speech (TTS) tasks, how to generate a more emotional and
more expressive speech is becoming a new challenge to researchers due
to the scarcity of high-quality emotion... | 2106.09317 | title_snapshot |
rallabandi21_interspeech | Perception of Social Speaker Characteristics in Synthetic Speech | [
"Sai Sirisha Rallabandi",
"Abhinav Bharadwaj",
"Babak Naderi",
"Sebastian Möller"
] | https://www.isca-archive.org/interspeech_2021/rallabandi21_interspeech.html | https://www.isca-archive.org/interspeech_2021/rallabandi21_interspeech.pdf | 10.21437/Interspeech.2021-1229 | 2771-2775 | @inproceedings{rallabandi21_interspeech,
title = {{Perception of Social Speaker Characteristics in Synthetic Speech}},
author = {Sai Sirisha Rallabandi and Abhinav Bharadwaj and Babak Naderi and Sebastian Möller},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2771--2775},
doi ... | With the improved computational abilities, the usage of chatbots and
conversational agents has become more prevalent. Therefore, it is essential
that these agents exhibit certain social speaker characteristics in
the generated speech. In this paper, we study the perception of such
speaker characteristics in two commerc... | null | null |
bakhturina21_interspeech | Hi-Fi Multi-Speaker English TTS Dataset | [
"Evelina Bakhturina",
"Vitaly Lavrukhin",
"Boris Ginsburg",
"Yang Zhang"
] | https://www.isca-archive.org/interspeech_2021/bakhturina21_interspeech.html | https://www.isca-archive.org/interspeech_2021/bakhturina21_interspeech.pdf | 10.21437/Interspeech.2021-1599 | 2776-2780 | @inproceedings{bakhturina21_interspeech,
title = {{Hi-Fi Multi-Speaker English TTS Dataset}},
author = {Evelina Bakhturina and Vitaly Lavrukhin and Boris Ginsburg and Yang Zhang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2776--2780},
doi = {10.21437/Interspeech.2021-15... | This paper introduces a new multi-speaker English dataset for training
text-to-speech models. The dataset is based on LibriVox audiobooks
and Project Gutenberg texts, both in the public domain. The new dataset
contains about 292 hours of speech from 10 speakers with at least 17
hours per speaker sampled at 44.1 kHz. To... | 2104.01497 | title_snapshot |
tseng21b_interspeech | Utilizing Self-Supervised Representations for MOS Prediction | [
"Wei-Cheng Tseng",
"Chien-yu Huang",
"Wei-Tsung Kao",
"Yist Y. Lin",
"Hung-yi Lee"
] | https://www.isca-archive.org/interspeech_2021/tseng21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/tseng21b_interspeech.pdf | 10.21437/Interspeech.2021-2013 | 2781-2785 | @inproceedings{tseng21b_interspeech,
title = {{Utilizing Self-Supervised Representations for MOS Prediction}},
author = {Wei-Cheng Tseng and Chien-yu Huang and Wei-Tsung Kao and Yist Y. Lin and Hung-yi Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2781--2785},
doi = {... | Speech quality assessment has been a critical issue in speech processing
for decades. Existing automatic evaluations usually require clean references
or parallel ground truth data, which is infeasible when the amount
of data soars. Subjective tests, on the other hand, do not need any
additional clean or parallel data a... | 2104.03017 | title_snapshot |
mussakhojayeva21_interspeech | KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis Dataset | [
"Saida Mussakhojayeva",
"Aigerim Janaliyeva",
"Almas Mirzakhmetov",
"Yerbolat Khassanov",
"Huseyin Atakan Varol"
] | https://www.isca-archive.org/interspeech_2021/mussakhojayeva21_interspeech.html | https://www.isca-archive.org/interspeech_2021/mussakhojayeva21_interspeech.pdf | 10.21437/Interspeech.2021-2124 | 2786-2790 | @inproceedings{mussakhojayeva21_interspeech,
title = {{KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis Dataset}},
author = {Saida Mussakhojayeva and Aigerim Janaliyeva and Almas Mirzakhmetov and Yerbolat Khassanov and Huseyin Atakan Varol},
year = {2021},
booktitle = {{Interspeech 2021}},
... | This paper introduces a high-quality open-source speech synthesis dataset
for Kazakh, a low-resource language spoken by over 13 million people
worldwide. The dataset consists of about 93 hours of transcribed audio
recordings spoken by two professional speakers (female and male). It
is the first publicly available large... | 2104.08459 | title_snapshot |
taylor21_interspeech | Confidence Intervals for ASR-Based TTS Evaluation | [
"Jason Taylor",
"Korin Richmond"
] | https://www.isca-archive.org/interspeech_2021/taylor21_interspeech.html | https://www.isca-archive.org/interspeech_2021/taylor21_interspeech.pdf | 10.21437/Interspeech.2021-2203 | 2791-2795 | @inproceedings{taylor21_interspeech,
title = {{Confidence Intervals for ASR-Based TTS Evaluation}},
author = {Jason Taylor and Korin Richmond},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2791--2795},
doi = {10.21437/Interspeech.2021-2203},
issn = {2958-1796},
} | Automatic speech recognition (ASR) is increasingly used to evaluate
the intelligibility of text-to-speech synthesis (TTS). ASR is less
costly than traditional listening tests, but questions remain about
its reliability. We re-evaluate the Blizzard Challenge’s intelligibility
tasks in English since 2011 using ASR. Re-an... | null | null |
reddy21_interspeech | INTERSPEECH 2021 Deep Noise Suppression Challenge | [
"Chandan K.A. Reddy",
"Harishchandra Dubey",
"Kazuhito Koishida",
"Arun Nair",
"Vishak Gopal",
"Ross Cutler",
"Sebastian Braun",
"Hannes Gamper",
"Robert Aichner",
"Sriram Srinivasan"
] | https://www.isca-archive.org/interspeech_2021/reddy21_interspeech.html | https://www.isca-archive.org/interspeech_2021/reddy21_interspeech.pdf | 10.21437/Interspeech.2021-1609 | 2796-2800 | @inproceedings{reddy21_interspeech,
title = {{INTERSPEECH 2021 Deep Noise Suppression Challenge}},
author = {Chandan K.A. Reddy and Harishchandra Dubey and Kazuhito Koishida and Arun Nair and Vishak Gopal and Ross Cutler and Sebastian Braun and Hannes Gamper and Robert Aichner and Sriram Srinivasan},
year ... | The Deep Noise Suppression (DNS) challenge was designed to unify the
research efforts in the area of noise suppression targeted for human
perception. We recently organized a DNS challenge special session at
INTERSPEECH 2020 and ICASSP 2021. We open-sourced training and test
datasets for the wideband scenario along with... | 2101.01902 | title_snapshot |
li21g_interspeech | A Simultaneous Denoising and Dereverberation Framework with Target Decoupling | [
"Andong Li",
"Wenzhe Liu",
"Xiaoxue Luo",
"Guochen Yu",
"Chengshi Zheng",
"Xiaodong Li"
] | https://www.isca-archive.org/interspeech_2021/li21g_interspeech.html | https://www.isca-archive.org/interspeech_2021/li21g_interspeech.pdf | 10.21437/Interspeech.2021-1137 | 2801-2805 | @inproceedings{li21g_interspeech,
title = {{A Simultaneous Denoising and Dereverberation Framework with Target Decoupling}},
author = {Andong Li and Wenzhe Liu and Xiaoxue Luo and Guochen Yu and Chengshi Zheng and Xiaodong Li},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2801--28... | Background noise and room reverberation are regarded as two major factors
to degrade the subjective speech quality. In this paper, we propose
an integrated framework to address simultaneous denoising and dereverberation
under complicated scenario environments. It adopts a chain optimization
strategy and designs four su... | 2106.12743 | title_snapshot |
xu21h_interspeech | Deep Noise Suppression with Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data | [
"Ziyi Xu",
"Maximilian Strake",
"Tim Fingscheidt"
] | https://www.isca-archive.org/interspeech_2021/xu21h_interspeech.html | https://www.isca-archive.org/interspeech_2021/xu21h_interspeech.pdf | 10.21437/Interspeech.2021-936 | 2806-2810 | @inproceedings{xu21h_interspeech,
title = {{Deep Noise Suppression with Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data}},
author = {Ziyi Xu and Maximilian Strake and Tim Fingscheidt},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2806--2810},
doi =... | Data-driven speech enhancement employing deep neural networks (DNNs)
can provide state-of-the-art performance even in the presence of non-stationary
noise. During the training process, most of the speech enhancement
neural networks are trained in a fully supervised way with losses requiring
noisy speech to be synthesiz... | 2103.17088 | title_snapshot |
le21b_interspeech | DPCRN: Dual-Path Convolution Recurrent Network for Single Channel Speech Enhancement | [
"Xiaohuai Le",
"Hongsheng Chen",
"Kai Chen",
"Jing Lu"
] | https://www.isca-archive.org/interspeech_2021/le21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/le21b_interspeech.pdf | 10.21437/Interspeech.2021-296 | 2811-2815 | @inproceedings{le21b_interspeech,
title = {{DPCRN: Dual-Path Convolution Recurrent Network for Single Channel Speech Enhancement}},
author = {Xiaohuai Le and Hongsheng Chen and Kai Chen and Jing Lu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2811--2815},
doi = {10.21437... | The dual-path RNN (DPRNN) was proposed to more effectively model extremely
long sequences for speech separation in the time domain. By splitting
long sequences to smaller chunks and applying intra-chunk and inter-chunk
RNNs, the DPRNN reached promising performance in speech separation
with a limited model size. In this... | 2107.05429 | title_snapshot |
lv21_interspeech | DCCRN+: Channel-Wise Subband DCCRN with SNR Estimation for Speech Enhancement | [
"Shubo Lv",
"Yanxin Hu",
"Shimin Zhang",
"Lei Xie"
] | https://www.isca-archive.org/interspeech_2021/lv21_interspeech.html | https://www.isca-archive.org/interspeech_2021/lv21_interspeech.pdf | 10.21437/Interspeech.2021-1482 | 2816-2820 | @inproceedings{lv21_interspeech,
title = {{DCCRN+: Channel-Wise Subband DCCRN with SNR Estimation for Speech Enhancement}},
author = {Shubo Lv and Yanxin Hu and Shimin Zhang and Lei Xie},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2816--2820},
doi = {10.21437/Interspeech... | Deep complex convolution recurrent network (DCCRN), which extends CRN
with complex structure, has achieved superior performance in MOS evaluation
in Interspeech 2020 deep noise suppression challenge (DNS2020). This
paper further extends DCCRN with the following significant revisions.
We first extend the model to sub-ba... | 2106.08672 | title_snapshot |
zhang21s_interspeech | DBNet: A Dual-Branch Network Architecture Processing on Spectrum and Waveform for Single-Channel Speech Enhancement | [
"Kanghao Zhang",
"Shulin He",
"Hao Li",
"Xueliang Zhang"
] | https://www.isca-archive.org/interspeech_2021/zhang21s_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21s_interspeech.pdf | 10.21437/Interspeech.2021-1042 | 2821-2825 | @inproceedings{zhang21s_interspeech,
title = {{DBNet: A Dual-Branch Network Architecture Processing on Spectrum and Waveform for Single-Channel Speech Enhancement}},
author = {Kanghao Zhang and Shulin He and Hao Li and Xueliang Zhang},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {... | In real acoustic environment, speech enhancement is an arduous task
to improve the quality and intelligibility of speech interfered by
background noise and reverberation. Over the past years, deep learning
has shown great potential on speech enhancement. In this paper, we
propose a novel real-time framework called DBNe... | 2105.02436 | title_snapshot |
zhang21t_interspeech | Low-Delay Speech Enhancement Using Perceptually Motivated Target and Loss | [
"Xu Zhang",
"Xinlei Ren",
"Xiguang Zheng",
"Lianwu Chen",
"Chen Zhang",
"Liang Guo",
"Bing Yu"
] | https://www.isca-archive.org/interspeech_2021/zhang21t_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhang21t_interspeech.pdf | 10.21437/Interspeech.2021-1410 | 2826-2830 | @inproceedings{zhang21t_interspeech,
title = {{Low-Delay Speech Enhancement Using Perceptually Motivated Target and Loss}},
author = {Xu Zhang and Xinlei Ren and Xiguang Zheng and Lianwu Chen and Chen Zhang and Liang Guo and Bing Yu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2... | Speech enhancement approaches based on deep neural network have outperformed
the traditional signal processing methods. This paper presents a low-delay
speech enhancement method that employs a new perceptually motivated
training target and loss function. The proposed approach can achieve
similar speech enhancement perf... | null | null |
oostermeijer21_interspeech | Lightweight Causal Transformer with Local Self-Attention for Real-Time Speech Enhancement | [
"Koen Oostermeijer",
"Qing Wang",
"Jun Du"
] | https://www.isca-archive.org/interspeech_2021/oostermeijer21_interspeech.html | https://www.isca-archive.org/interspeech_2021/oostermeijer21_interspeech.pdf | 10.21437/Interspeech.2021-668 | 2831-2835 | @inproceedings{oostermeijer21_interspeech,
title = {{Lightweight Causal Transformer with Local Self-Attention for Real-Time Speech Enhancement}},
author = {Koen Oostermeijer and Qing Wang and Jun Du},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2831--2835},
doi = {10.2143... | In this paper, we describe a novel speech enhancement transformer architecture.
The model uses local causal self-attention, which makes it lightweight
and therefore particularly well-suited for real-time speech enhancement
in computation resource-limited environments. In addition, we provide
several ablation studies th... | null | null |
ristea21_interspeech | Self-Paced Ensemble Learning for Speech and Audio Classification | [
"Nicolae-Cătălin Ristea",
"Radu Tudor Ionescu"
] | https://www.isca-archive.org/interspeech_2021/ristea21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ristea21_interspeech.pdf | 10.21437/Interspeech.2021-155 | 2836-2840 | @inproceedings{ristea21_interspeech,
title = {{Self-Paced Ensemble Learning for Speech and Audio Classification}},
author = {Nicolae-Cătălin Ristea and Radu Tudor Ionescu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2836--2840},
doi = {10.21437/Interspeech.2021-155},
i... | Combining multiple machine learning models into an ensemble is known
to provide superior performance levels compared to the individual components
forming the ensemble. This is because models can complement each other
in taking better decisions. Instead of just combining the models, we
propose a self-paced ensemble lear... | 2103.11988 | title_snapshot |
kojima21_interspeech | Knowledge Distillation for Streaming Transformer–Transducer | [
"Atsushi Kojima"
] | https://www.isca-archive.org/interspeech_2021/kojima21_interspeech.html | https://www.isca-archive.org/interspeech_2021/kojima21_interspeech.pdf | 10.21437/Interspeech.2021-175 | 2841-2845 | @inproceedings{kojima21_interspeech,
title = {{Knowledge Distillation for Streaming Transformer–Transducer}},
author = {Atsushi Kojima},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2841--2845},
doi = {10.21437/Interspeech.2021-175},
issn = {2958-1796},
} | We explore knowledge distillation methods from nonstreaming to streaming
Transformer–Transducer (T–T) models. Streaming T–T
truncates future context. It leads to recognition quality degradation
compared with the original T–T. In this work, we explore knowledge
distillation, which minimizes internal representations in a... | null | null |
lohrenz21_interspeech | Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition | [
"Timo Lohrenz",
"Zhengyang Li",
"Tim Fingscheidt"
] | https://www.isca-archive.org/interspeech_2021/lohrenz21_interspeech.html | https://www.isca-archive.org/interspeech_2021/lohrenz21_interspeech.pdf | 10.21437/Interspeech.2021-555 | 2846-2850 | @inproceedings{lohrenz21_interspeech,
title = {{Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition}},
author = {Timo Lohrenz and Zhengyang Li and Tim Fingscheidt},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2846--2850},
doi ... | Stream fusion, also known as system combination, is a common technique
in automatic speech recognition for traditional hybrid hidden Markov
model approaches, yet mostly unexplored for modern deep neural network
end-to-end model architectures. Here, we investigate various fusion
techniques for the all-attention-based en... | 2104.00120 | title_snapshot |
zaiem21_interspeech | Conditional Independence for Pretext Task Selection in Self-Supervised Speech Representation Learning | [
"Salah Zaiem",
"Titouan Parcollet",
"Slim Essid"
] | https://www.isca-archive.org/interspeech_2021/zaiem21_interspeech.html | https://www.isca-archive.org/interspeech_2021/zaiem21_interspeech.pdf | 10.21437/Interspeech.2021-1027 | 2851-2855 | @inproceedings{zaiem21_interspeech,
title = {{Conditional Independence for Pretext Task Selection in Self-Supervised Speech Representation Learning}},
author = {Salah Zaiem and Titouan Parcollet and Slim Essid},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2851--2855},
doi ... | Through solving pretext tasks, self-supervised learning (SSL) leverages
unlabeled data to extract useful latent representations replacing traditional
input features in the downstream task. A common pretext task consists
in pretraining a SSL model on pseudo-labels derived from the original
signal. This technique is part... | 2104.07388 | title_snapshot |
zeineldeen21_interspeech | Investigating Methods to Improve Language Model Integration for Attention-Based Encoder-Decoder ASR Models | [
"Mohammad Zeineldeen",
"Aleksandr Glushko",
"Wilfried Michel",
"Albert Zeyer",
"Ralf Schlüter",
"Hermann Ney"
] | https://www.isca-archive.org/interspeech_2021/zeineldeen21_interspeech.html | https://www.isca-archive.org/interspeech_2021/zeineldeen21_interspeech.pdf | 10.21437/Interspeech.2021-1255 | 2856-2860 | @inproceedings{zeineldeen21_interspeech,
title = {{Investigating Methods to Improve Language Model Integration for Attention-Based Encoder-Decoder ASR Models}},
author = {Mohammad Zeineldeen and Aleksandr Glushko and Wilfried Michel and Albert Zeyer and Ralf Schlüter and Hermann Ney},
year = {2021},
... | Attention-based encoder-decoder (AED) models learn an implicit internal
language model (ILM) from the training transcriptions. The integration
with an external LM trained on much more unpaired text usually leads
to better performance. A Bayesian interpretation as in the hybrid autoregressive
transducer (HAT) suggests d... | 2104.05544 | title_snapshot |
vyas21b_interspeech | Comparing CTC and LFMMI for Out-of-Domain Adaptation of wav2vec 2.0 Acoustic Model | [
"Apoorv Vyas",
"Srikanth Madikeri",
"Hervé Bourlard"
] | https://www.isca-archive.org/interspeech_2021/vyas21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/vyas21b_interspeech.pdf | 10.21437/Interspeech.2021-1683 | 2861-2865 | @inproceedings{vyas21b_interspeech,
title = {{Comparing CTC and LFMMI for Out-of-Domain Adaptation of wav2vec 2.0 Acoustic Model}},
author = {Apoorv Vyas and Srikanth Madikeri and Hervé Bourlard},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2861--2865},
doi = {10.21437/In... | In this work, we investigate if the wav2vec 2.0 self-supervised pretraining
helps mitigate the overfitting issues with connectionist temporal classification
(CTC) training to reduce its performance gap with flat-start lattice-free
MMI (E2E-LFMMI) for automatic speech recognition with limited training
data. Towards that... | 2104.02558 | title_snapshot |
moine21_interspeech | Speaker Attentive Speech Emotion Recognition | [
"Clément Le Moine",
"Nicolas Obin",
"Axel Roebel"
] | https://www.isca-archive.org/interspeech_2021/moine21_interspeech.html | https://www.isca-archive.org/interspeech_2021/moine21_interspeech.pdf | 10.21437/Interspeech.2021-573 | 2866-2870 | @inproceedings{moine21_interspeech,
title = {{Speaker Attentive Speech Emotion Recognition}},
author = {Clément Le Moine and Nicolas Obin and Axel Roebel},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2866--2870},
doi = {10.21437/Interspeech.2021-573},
issn = {2958-... | Speech Emotion Recognition (SER) task has known significant improvements
over the last years with the advent of Deep Neural Networks (DNNs).
However, even the most successful methods are still rather failing
when adaptation to specific speakers and scenarios is needed, inevitably
leading to poorer performances when com... | 2104.07288 | title_snapshot |
leem21_interspeech | Separation of Emotional and Reconstruction Embeddings on Ladder Network to Improve Speech Emotion Recognition Robustness in Noisy Conditions | [
"Seong-Gyun Leem",
"Daniel Fulford",
"Jukka-Pekka Onnela",
"David Gard",
"Carlos Busso"
] | https://www.isca-archive.org/interspeech_2021/leem21_interspeech.html | https://www.isca-archive.org/interspeech_2021/leem21_interspeech.pdf | 10.21437/Interspeech.2021-1438 | 2871-2875 | @inproceedings{leem21_interspeech,
title = {{Separation of Emotional and Reconstruction Embeddings on Ladder Network to Improve Speech Emotion Recognition Robustness in Noisy Conditions}},
author = {Seong-Gyun Leem and Daniel Fulford and Jukka-Pekka Onnela and David Gard and Carlos Busso},
year = {202... | When speech emotion recognition (SER) is applied in an actual
application, the system should be able to cope with audio acquired
in a noisy, unconstrained environment. Most studies on noise-robust
SER require a parallel dataset with emotion labels, which is impractical
to collect, or use speech with artificially added ... | null | null |
georgiou21_interspeech | M: MultiModal Masking Applied to Sentiment Analysis | [
"Efthymios Georgiou",
"Georgios Paraskevopoulos",
"Alexandros Potamianos"
] | https://www.isca-archive.org/interspeech_2021/georgiou21_interspeech.html | https://www.isca-archive.org/interspeech_2021/georgiou21_interspeech.pdf | 10.21437/Interspeech.2021-1739 | 2876-2880 | @inproceedings{georgiou21_interspeech,
title = {{M3: MultiModal Masking Applied to Sentiment Analysis}},
author = {Efthymios Georgiou and Georgios Paraskevopoulos and Alexandros Potamianos},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2876--2880},
doi = {10.21437/Interspe... | A common issue when training multimodal architectures is that not all
modalities contribute equally to the model’s prediction and the
network tends to over-rely on the strongest modality. In this work,
we present M 3 , a training procedure based on modality masking
for deep multimodal architectures. During network trai... | null | null |
klejch21_interspeech | The CSTR System for Multilingual and Code-Switching ASR Challenges for Low Resource Indian Languages | [
"Ondřej Klejch",
"Electra Wallington",
"Peter Bell"
] | https://www.isca-archive.org/interspeech_2021/klejch21_interspeech.html | https://www.isca-archive.org/interspeech_2021/klejch21_interspeech.pdf | 10.21437/Interspeech.2021-1035 | 2881-2885 | @inproceedings{klejch21_interspeech,
title = {{The CSTR System for Multilingual and Code-Switching ASR Challenges for Low Resource Indian Languages}},
author = {Ondřej Klejch and Electra Wallington and Peter Bell},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2881--2885},
doi ... | This paper describes the CSTR submission to the Multilingual and Code-Switching
ASR Challenges at Interspeech 2021. For the multilingual track of the
challenge, we trained a multilingual CNN-TDNN acoustic model for Gujarati,
Hindi, Marathi, Odia, Tamil and Telugu and subsequently fine-tuned
the model on monolingual tra... | null | null |
zhou21d_interspeech | Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition | [
"Wei Zhou",
"Mohammad Zeineldeen",
"Zuoyun Zheng",
"Ralf Schlüter",
"Hermann Ney"
] | https://www.isca-archive.org/interspeech_2021/zhou21d_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhou21d_interspeech.pdf | 10.21437/Interspeech.2021-1623 | 2886-2890 | @inproceedings{zhou21d_interspeech,
title = {{Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition}},
author = {Wei Zhou and Mohammad Zeineldeen and Zuoyun Zheng and Ralf Schlüter and Hermann Ney},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2886--2890},
doi ... | Subword units are commonly used for end-to-end automatic speech recognition
(ASR), while a fully acoustic-oriented subword modeling approach is
somewhat missing. We propose an acoustic data-driven subword modeling
(ADSM) approach that adapts the advantages of several text-based and
acoustic-based subword methods into o... | 2104.09106 | title_snapshot |
zhou21e_interspeech | Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept | [
"Wei Zhou",
"Albert Zeyer",
"André Merboldt",
"Ralf Schlüter",
"Hermann Ney"
] | https://www.isca-archive.org/interspeech_2021/zhou21e_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhou21e_interspeech.pdf | 10.21437/Interspeech.2021-1671 | 2891-2895 | @inproceedings{zhou21e_interspeech,
title = {{Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept}},
author = {Wei Zhou and Albert Zeyer and André Merboldt and Ralf Schlüter and Hermann Ney},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2891--2895},
doi ... | With the advent of direct models in automatic speech recognition (ASR),
the formerly prevalent frame-wise acoustic modeling based on hidden
Markov models (HMM) diversified into a number of modeling architectures
like encoder-decoder attention models, transducer models and segmental
models (direct HMM). While transducer... | 2104.06104 | title_snapshot |
khosravani21_interspeech | Modeling Dialectal Variation for Swiss German Automatic Speech Recognition | [
"Abbas Khosravani",
"Philip N. Garner",
"Alexandros Lazaridis"
] | https://www.isca-archive.org/interspeech_2021/khosravani21_interspeech.html | https://www.isca-archive.org/interspeech_2021/khosravani21_interspeech.pdf | 10.21437/Interspeech.2021-1735 | 2896-2900 | @inproceedings{khosravani21_interspeech,
title = {{Modeling Dialectal Variation for Swiss German Automatic Speech Recognition}},
author = {Abbas Khosravani and Philip N. Garner and Alexandros Lazaridis},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2896--2900},
doi = {10.2... | We describe a speech recognition system for Swiss German, a dialectal
spoken language in German-speaking Switzerland. Swiss German has no
standard orthography, with a significant variation in its written form.
To alleviate the uncertainty associated with this variability, we automatically
generate a lexicon from which ... | null | null |
egorova21_interspeech | Out-of-Vocabulary Words Detection with Attention and CTC Alignments in an End-to-End ASR System | [
"Ekaterina Egorova",
"Hari Krishna Vydana",
"Lukáš Burget",
"Jan Černocký"
] | https://www.isca-archive.org/interspeech_2021/egorova21_interspeech.html | https://www.isca-archive.org/interspeech_2021/egorova21_interspeech.pdf | 10.21437/Interspeech.2021-1756 | 2901-2905 | @inproceedings{egorova21_interspeech,
title = {{Out-of-Vocabulary Words Detection with Attention and CTC Alignments in an End-to-End ASR System}},
author = {Ekaterina Egorova and Hari Krishna Vydana and Lukáš Burget and Jan Černocký},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2... | This work explores the effectiveness of detecting positions of out-of-vocabulary
words (OOVs) in a decoded utterance using attention weights and CTC
per-frame outputs of an end-to-end system predicting word sequences.
We show that the end-to-end approach can be effective for the task
of OOV detection. CTC alignments ar... | null | null |
wiesner21_interspeech | Training Hybrid Models on Noisy Transliterated Transcripts for Code-Switched Speech Recognition | [
"Matthew Wiesner",
"Mousmita Sarma",
"Ashish Arora",
"Desh Raj",
"Dongji Gao",
"Ruizhe Huang",
"Supreet Preet",
"Moris Johnson",
"Zikra Iqbal",
"Nagendra Goel",
"Jan Trmal",
"Leibny Paola García Perera",
"Sanjeev Khudanpur"
] | https://www.isca-archive.org/interspeech_2021/wiesner21_interspeech.html | https://www.isca-archive.org/interspeech_2021/wiesner21_interspeech.pdf | 10.21437/Interspeech.2021-2127 | 2906-2910 | @inproceedings{wiesner21_interspeech,
title = {{Training Hybrid Models on Noisy Transliterated Transcripts for Code-Switched Speech Recognition}},
author = {Matthew Wiesner and Mousmita Sarma and Ashish Arora and Desh Raj and Dongji Gao and Ruizhe Huang and Supreet Preet and Moris Johnson and Zikra Iqbal and... | In this paper, we describe the JHU-GoVivace submission for subtask
2 (code-switching task) of the Multilingual and Code-switching ASR
challenges for low resource Indian languages. We built a hybrid HMM-DNN
system with several improvements over the provided baseline in terms
of lexical, language, and acoustic modeling. ... | null | null |
xue21c_interspeech | Speech Intelligibility of Dysarthric Speech: Human Scores and Acoustic-Phonetic Features | [
"Wei Xue",
"Roeland van Hout",
"Fleur Boogmans",
"Mario Ganzeboom",
"Catia Cucchiarini",
"Helmer Strik"
] | https://www.isca-archive.org/interspeech_2021/xue21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/xue21c_interspeech.pdf | 10.21437/Interspeech.2021-1189 | 2911-2915 | @inproceedings{xue21c_interspeech,
title = {{Speech Intelligibility of Dysarthric Speech: Human Scores and Acoustic-Phonetic Features}},
author = {Wei Xue and Roeland van Hout and Fleur Boogmans and Mario Ganzeboom and Catia Cucchiarini and Helmer Strik},
year = {2021},
booktitle = {{Interspeech 202... | We investigated speech intelligibility in dysarthric and non-dysarthric
speakers as measured by two commonly used metrics, ratings through
the Visual Analogue Scale (VAS) and word accuracy (AcW) through orthographic
transcriptions. To gain a better understanding of how acoustic-phonetic
correlates could be employed to ... | null | null |
kim21i_interspeech | Analyzing Short Term Dynamic Speech Features for Understanding Behavioral Traits of Children with Autism Spectrum Disorder | [
"Young-Kyung Kim",
"Rimita Lahiri",
"Md. Nasir",
"So Hyun Kim",
"Somer Bishop",
"Catherine Lord",
"Shrikanth S. Narayanan"
] | https://www.isca-archive.org/interspeech_2021/kim21i_interspeech.html | https://www.isca-archive.org/interspeech_2021/kim21i_interspeech.pdf | 10.21437/Interspeech.2021-2111 | 2916-2920 | @inproceedings{kim21i_interspeech,
title = {{Analyzing Short Term Dynamic Speech Features for Understanding Behavioral Traits of Children with Autism Spectrum Disorder}},
author = {Young-Kyung Kim and Rimita Lahiri and Md. Nasir and So Hyun Kim and Somer Bishop and Catherine Lord and Shrikanth S. Narayanan},... | Computational methodologies have shown promise in advancing diagnostic
and intervention research in the domain of Autism Spectrum Disorder
(ASD) . Prior works have investigated speech features to assess
disorder severity and also to differentiate between children with and
without an ASD diagnosis. In this work, we expl... | null | null |
jesko21_interspeech | Vocalization Recognition of People with Profound Intellectual and Multiple Disabilities (PIMD) Using Machine Learning Algorithms | [
"Waldemar Jęśko"
] | https://www.isca-archive.org/interspeech_2021/jesko21_interspeech.html | https://www.isca-archive.org/interspeech_2021/jesko21_interspeech.pdf | 10.21437/Interspeech.2021-1239 | 2921-2925 | @inproceedings{jesko21_interspeech,
title = {{Vocalization Recognition of People with Profound Intellectual and Multiple Disabilities (PIMD) Using Machine Learning Algorithms}},
author = {Waldemar Jęśko},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2921--2925},
doi = {10.... | We investigate vocalization recognition for people with Profound Intellectual
and Multiple Disabilities using various machine learning algorithms.
The amount of training data available for people with PIMD is typically
significantly limited. Due to this fact, data augmentation process
was used. Various types of Machine... | null | null |
fivela21_interspeech | Phonetic Complexity, Speech Accuracy and Intelligibility Assessment of Italian Dysarthric Speech | [
"Barbara Gili Fivela",
"Vincenzo Sallustio",
"Silvia Pede",
"Danilo Patrocinio"
] | https://www.isca-archive.org/interspeech_2021/fivela21_interspeech.html | https://www.isca-archive.org/interspeech_2021/fivela21_interspeech.pdf | 10.21437/Interspeech.2021-1862 | 2926-2930 | @inproceedings{fivela21_interspeech,
title = {{Phonetic Complexity, Speech Accuracy and Intelligibility Assessment of Italian Dysarthric Speech}},
author = {Barbara Gili Fivela and Vincenzo Sallustio and Silvia Pede and Danilo Patrocinio},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | Intelligibility is the degree to which the speech of a person may be
understood by a listener, and is related to functional limitation and
disability. In protocols for the clinical assessment of dysarthria,
intelligibility checks are included, as well as evaluations of speech
accuracy, which is more directly related to... | null | null |
ng21_interspeech | Detection of Consonant Errors in Disordered Speech Based on Consonant-Vowel Segment Embedding | [
"Si-Ioi Ng",
"Cymie Wing-Yee Ng",
"Jingyu Li",
"Tan Lee"
] | https://www.isca-archive.org/interspeech_2021/ng21_interspeech.html | https://www.isca-archive.org/interspeech_2021/ng21_interspeech.pdf | 10.21437/Interspeech.2021-1305 | 2931-2935 | @inproceedings{ng21_interspeech,
title = {{Detection of Consonant Errors in Disordered Speech Based on Consonant-Vowel Segment Embedding}},
author = {Si-Ioi Ng and Cymie Wing-Yee Ng and Jingyu Li and Tan Lee},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2931--2935},
doi =... | Speech sound disorder (SSD) refers to a type of developmental disorder
in young children who encounter persistent difficulties in producing
certain speech sounds at the expected age. Consonant errors are the
major indicator of SSD in clinical assessment. Previous studies on
automatic assessment of SSD revealed that det... | 2106.08536 | title_snapshot |
hair21_interspeech | Assessing Posterior-Based Mispronunciation Detection on Field-Collected Recordings from Child Speech Therapy Sessions | [
"Adam Hair",
"Guanlong Zhao",
"Beena Ahmed",
"Kirrie J. Ballard",
"Ricardo Gutierrez-Osuna"
] | https://www.isca-archive.org/interspeech_2021/hair21_interspeech.html | https://www.isca-archive.org/interspeech_2021/hair21_interspeech.pdf | 10.21437/Interspeech.2021-69 | 2936-2940 | @inproceedings{hair21_interspeech,
title = {{Assessing Posterior-Based Mispronunciation Detection on Field-Collected Recordings from Child Speech Therapy Sessions}},
author = {Adam Hair and Guanlong Zhao and Beena Ahmed and Kirrie J. Ballard and Ricardo Gutierrez-Osuna},
year = {2021},
booktitle = {... | A critical component of child speech therapy is home practice with
a caregiver, who can provide feedback. However, caregivers oftentimes
struggle with accurately rating speech and with perceiving pronunciation
errors. One potential solution for this issue is to embed automatic
mispronunciation-detection (MPD) algorithm... | null | null |
mirheidari21_interspeech | Identifying Cognitive Impairment Using Sentence Representation Vectors | [
"Bahman Mirheidari",
"Yilin Pan",
"Daniel Blackburn",
"Ronan O’Malley",
"Heidi Christensen"
] | https://www.isca-archive.org/interspeech_2021/mirheidari21_interspeech.html | https://www.isca-archive.org/interspeech_2021/mirheidari21_interspeech.pdf | 10.21437/Interspeech.2021-915 | 2941-2945 | @inproceedings{mirheidari21_interspeech,
title = {{Identifying Cognitive Impairment Using Sentence Representation Vectors}},
author = {Bahman Mirheidari and Yilin Pan and Daniel Blackburn and Ronan O’Malley and Heidi Christensen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2941-... | The widely used word vectors can be extended at the sentence level
to perform a wide range of natural language processing (NLP) tasks.
Recently the Bidirectional Encoder Representations from Transformers
(BERT) language representation achieved state-of-the-art performance
for these applications. The model is trained wi... | null | null |
yue21b_interspeech | Parental Spoken Scaffolding and Narrative Skills in Crowd-Sourced Storytelling Samples of Young Children | [
"Zhengjun Yue",
"Jon Barker",
"Heidi Christensen",
"Cristina McKean",
"Elaine Ashton",
"Yvonne Wren",
"Swapnil Gadgil",
"Rebecca Bright"
] | https://www.isca-archive.org/interspeech_2021/yue21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/yue21b_interspeech.pdf | 10.21437/Interspeech.2021-1297 | 2946-2950 | @inproceedings{yue21b_interspeech,
title = {{Parental Spoken Scaffolding and Narrative Skills in Crowd-Sourced Storytelling Samples of Young Children}},
author = {Zhengjun Yue and Jon Barker and Heidi Christensen and Cristina McKean and Elaine Ashton and Yvonne Wren and Swapnil Gadgil and Rebecca Bright},
... | A novel crowdsourcing project to gather children’s storytelling
based language samples using a mobile app was undertaken across the
United Kingdom. Parents’ scaffolding of children’s narratives
was observed in many of the samples. This study was designed to examine
the relationship of scaffolding and young children’s n... | null | null |
xia21_interspeech | Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data | [
"Tong Xia",
"Jing Han",
"Lorena Qendro",
"Ting Dang",
"Cecilia Mascolo"
] | https://www.isca-archive.org/interspeech_2021/xia21_interspeech.html | https://www.isca-archive.org/interspeech_2021/xia21_interspeech.pdf | 10.21437/Interspeech.2021-1320 | 2951-2955 | @inproceedings{xia21_interspeech,
title = {{Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data}},
author = {Tong Xia and Jing Han and Lorena Qendro and Ting Dang and Cecilia Mascolo},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2951--2955},
doi = {10.21437/In... | Recently, sound-based COVID-19 detection studies have shown great promise
to achieve scalable and prompt digital pre-screening. However, there
are still two unsolved issues hindering the practice. First, collected
datasets for model training are often imbalanced, with a considerably
smaller proportion of users tested p... | 2104.02005 | title_snapshot |
wang21u_interspeech | Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization | [
"Disong Wang",
"Liqun Deng",
"Yu Ting Yeung",
"Xiao Chen",
"Xunying Liu",
"Helen Meng"
] | https://www.isca-archive.org/interspeech_2021/wang21u_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21u_interspeech.pdf | 10.21437/Interspeech.2021-2139 | 2956-2960 | @inproceedings{wang21u_interspeech,
title = {{Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization}},
author = {Disong Wang and Liqun Deng and Yu Ting Yeung and Xiao Chen and Xunying Liu and Helen Meng},
year = {2021},
boo... | Dysarthric speech detection (DSD) systems aim to detect characteristics
of the neuromotor disorder from speech. Such systems are particularly
susceptible to domain mismatch where the training and testing data
come from the source and target domains respectively, but the two domains
may differ in terms of speech stimuli... | 2106.10127 | title_snapshot |
bhattacharjee21_interspeech | Source and Vocal Tract Cues for Speech-Based Classification of Patients with Parkinson’s Disease and Healthy Subjects | [
"Tanuka Bhattacharjee",
"Jhansi Mallela",
"Yamini Belur",
"Nalini Atchayaram",
"Ravi Yadav",
"Pradeep Reddy",
"Dipanjan Gope",
"Prasanta Kumar Ghosh"
] | https://www.isca-archive.org/interspeech_2021/bhattacharjee21_interspeech.html | https://www.isca-archive.org/interspeech_2021/bhattacharjee21_interspeech.pdf | 10.21437/Interspeech.2021-2008 | 2961-2965 | @inproceedings{bhattacharjee21_interspeech,
title = {{Source and Vocal Tract Cues for Speech-Based Classification of Patients with Parkinson’s Disease and Healthy Subjects}},
author = {Tanuka Bhattacharjee and Jhansi Mallela and Yamini Belur and Nalini Atchayaram and Ravi Yadav and Pradeep Reddy and Dipanjan... | Parkinson’s disease (PD) affects both source and vocal tract
components of speech. Various speech cues explored in literature for
automatic classification of individuals with PD and healthy controls
(HC) implicitly carry information about both these components. This
work explicitly analyzes the contribution of source a... | null | null |
haulcy21_interspeech | CLAC: A Speech Corpus of Healthy English Speakers | [
"R’mani Haulcy",
"James Glass"
] | https://www.isca-archive.org/interspeech_2021/haulcy21_interspeech.html | https://www.isca-archive.org/interspeech_2021/haulcy21_interspeech.pdf | 10.21437/Interspeech.2021-1810 | 2966-2970 | @inproceedings{haulcy21_interspeech,
title = {{CLAC: A Speech Corpus of Healthy English Speakers}},
author = {R’mani Haulcy and James Glass},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2966--2970},
doi = {10.21437/Interspeech.2021-1810},
issn = {2958-1796},
} | This paper introduces the Crowdsourced Language Assessment Corpus (CLAC),
a speech corpus consisting of audio recordings and automatically-generated
transcripts for several speech and language tasks, as well as metadata
for each of the speakers. The CLAC was created to provide the community
with a collection of audio s... | null | null |
nortje21_interspeech | Direct Multimodal Few-Shot Learning of Speech and Images | [
"Leanne Nortje",
"Herman Kamper"
] | https://www.isca-archive.org/interspeech_2021/nortje21_interspeech.html | https://www.isca-archive.org/interspeech_2021/nortje21_interspeech.pdf | 10.21437/Interspeech.2021-49 | 2971-2975 | @inproceedings{nortje21_interspeech,
title = {{Direct Multimodal Few-Shot Learning of Speech and Images}},
author = {Leanne Nortje and Herman Kamper},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2971--2975},
doi = {10.21437/Interspeech.2021-49},
issn = {2958-1796},... | We propose direct multimodal few-shot models that learn a shared embedding
space of spoken words and images from only a few paired examples. Imagine
an agent is shown an image along with a spoken word describing the
object in the picture, e.g. pen, book and eraser . After
observing a few paired examples of each class, ... | 2012.05680 | title_snapshot |
sanabria21_interspeech | Talk, Don’t Write: A Study of Direct Speech-Based Image Retrieval | [
"Ramon Sanabria",
"Austin Waters",
"Jason Baldridge"
] | https://www.isca-archive.org/interspeech_2021/sanabria21_interspeech.html | https://www.isca-archive.org/interspeech_2021/sanabria21_interspeech.pdf | 10.21437/Interspeech.2021-96 | 2976-2980 | @inproceedings{sanabria21_interspeech,
title = {{Talk, Don’t Write: A Study of Direct Speech-Based Image Retrieval}},
author = {Ramon Sanabria and Austin Waters and Jason Baldridge},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2976--2980},
doi = {10.21437/Interspeech.2021... | Speech-based image retrieval has been studied as a proxy for joint
representation learning, usually without emphasis on retrieval itself.
As such, it is unclear how well speech-based retrieval can work in
practice — both in an absolute sense and versus alternative strategies
that combine automatic speech recognition (A... | 2104.01894 | title_snapshot |
zhao21b_interspeech | A Fast Discrete Two-Step Learning Hashing for Scalable Cross-Modal Retrieval | [
"Huan Zhao",
"Kaili Ma"
] | https://www.isca-archive.org/interspeech_2021/zhao21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/zhao21b_interspeech.pdf | 10.21437/Interspeech.2021-287 | 2981-2985 | @inproceedings{zhao21b_interspeech,
title = {{A Fast Discrete Two-Step Learning Hashing for Scalable Cross-Modal Retrieval}},
author = {Huan Zhao and Kaili Ma},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2981--2985},
doi = {10.21437/Interspeech.2021-287},
issn = {... | Recently, some cross-modal hashing methods are proposed to search data
for different modality effectively. Hashing has received wide attention
because of its low storage and high efficiency. Hashing-based methods
project the data instances from different modalities into a Hamming
space to learn hash codes for retrieval... | null | null |
wang21v_interspeech | Cross-Modal Knowledge Distillation Method for Automatic Cued Speech Recognition | [
"Jianrong Wang",
"Ziyue Tang",
"Xuewei Li",
"Mei Yu",
"Qiang Fang",
"Li Liu"
] | https://www.isca-archive.org/interspeech_2021/wang21v_interspeech.html | https://www.isca-archive.org/interspeech_2021/wang21v_interspeech.pdf | 10.21437/Interspeech.2021-432 | 2986-2990 | @inproceedings{wang21v_interspeech,
title = {{Cross-Modal Knowledge Distillation Method for Automatic Cued Speech Recognition}},
author = {Jianrong Wang and Ziyue Tang and Xuewei Li and Mei Yu and Qiang Fang and Li Liu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2986--2990},
... | Cued Speech (CS) is a visual communication system for the deaf or hearing
impaired people. It combines lip movements with hand cues to obtain
a complete phonetic repertoire. Current deep learning based methods
on automatic CS recognition suffer from a common problem, which is
the data scarcity. Until now, there are onl... | 2106.13686 | title_snapshot |
olaleye21_interspeech | Attention-Based Keyword Localisation in Speech Using Visual Grounding | [
"Kayode Olaleye",
"Herman Kamper"
] | https://www.isca-archive.org/interspeech_2021/olaleye21_interspeech.html | https://www.isca-archive.org/interspeech_2021/olaleye21_interspeech.pdf | 10.21437/Interspeech.2021-435 | 2991-2995 | @inproceedings{olaleye21_interspeech,
title = {{Attention-Based Keyword Localisation in Speech Using Visual Grounding}},
author = {Kayode Olaleye and Herman Kamper},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2991--2995},
doi = {10.21437/Interspeech.2021-435},
issn ... | Visually grounded speech models learn from images paired with spoken
captions. By tagging images with soft text labels using a trained visual
classifier with a fixed vocabulary, previous work has shown that it
is possible to train a model that can detect whether a particular
text keyword occurs in speech utterances or ... | 2106.08859 | title_snapshot |
khorrami21_interspeech | Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models | [
"Khazar Khorrami",
"Okko Räsänen"
] | https://www.isca-archive.org/interspeech_2021/khorrami21_interspeech.html | https://www.isca-archive.org/interspeech_2021/khorrami21_interspeech.pdf | 10.21437/Interspeech.2021-496 | 2996-3000 | @inproceedings{khorrami21_interspeech,
title = {{Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models}},
author = {Khazar Khorrami and Okko Räsänen},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {2996--3000},
doi = {10.21437/Interspeech.2021-496},
issn... | Systems that can find correspondences between multiple modalities,
such as between speech and images, have great potential to solve different
recognition and data analysis tasks in an unsupervised manner. This
work studies multimodal learning in the context of visually grounded
speech (VGS) models, and focuses on their... | 2108.02562 | title_snapshot |
chen21k_interspeech | Automatic Lip-Reading with Hierarchical Pyramidal Convolution and Self-Attention for Image Sequences with No Word Boundaries | [
"Hang Chen",
"Jun Du",
"Yu Hu",
"Li-Rong Dai",
"Bao-Cai Yin",
"Chin-Hui Lee"
] | https://www.isca-archive.org/interspeech_2021/chen21k_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21k_interspeech.pdf | 10.21437/Interspeech.2021-723 | 3001-3005 | @inproceedings{chen21k_interspeech,
title = {{Automatic Lip-Reading with Hierarchical Pyramidal Convolution and Self-Attention for Image Sequences with No Word Boundaries}},
author = {Hang Chen and Jun Du and Yu Hu and Li-Rong Dai and Bao-Cai Yin and Chin-Hui Lee},
year = {2021},
booktitle = {{Inter... | In this paper, we propose a novel deep learning architecture for improving
word-level lip-reading. We first incorporate multi-scale processing
into spatial feature extraction for lip-reading using hierarchical
pyramidal convolution (HPConv) and self-attention. Specifically, HPConv
is proposed to replace the conventiona... | 2012.14360 | title_judge |
rouditchenko21b_interspeech | Cascaded Multilingual Audio-Visual Learning from Videos | [
"Andrew Rouditchenko",
"Angie Boggust",
"David Harwath",
"Samuel Thomas",
"Hilde Kuehne",
"Brian Chen",
"Rameswar Panda",
"Rogerio Feris",
"Brian Kingsbury",
"Michael Picheny",
"James Glass"
] | https://www.isca-archive.org/interspeech_2021/rouditchenko21b_interspeech.html | https://www.isca-archive.org/interspeech_2021/rouditchenko21b_interspeech.pdf | 10.21437/Interspeech.2021-1352 | 3006-3010 | @inproceedings{rouditchenko21b_interspeech,
title = {{Cascaded Multilingual Audio-Visual Learning from Videos}},
author = {Andrew Rouditchenko and Angie Boggust and David Harwath and Samuel Thomas and Hilde Kuehne and Brian Chen and Rameswar Panda and Rogerio Feris and Brian Kingsbury and Michael Picheny and... | In this paper, we explore self-supervised audio-visual models that
learn from instructional videos. Prior work has shown that these models
can relate spoken words and sounds to visual content after training
on a large-scale dataset of videos, but they were only trained and
evaluated on videos in English. To learn multi... | 2111.04823 | title_snapshot |
ma21c_interspeech | LiRA: Learning Visual Speech Representations from Audio Through Self-Supervision | [
"Pingchuan Ma",
"Rodrigo Mira",
"Stavros Petridis",
"Björn W. Schuller",
"Maja Pantic"
] | https://www.isca-archive.org/interspeech_2021/ma21c_interspeech.html | https://www.isca-archive.org/interspeech_2021/ma21c_interspeech.pdf | 10.21437/Interspeech.2021-1360 | 3011-3015 | @inproceedings{ma21c_interspeech,
title = {{LiRA: Learning Visual Speech Representations from Audio Through Self-Supervision}},
author = {Pingchuan Ma and Rodrigo Mira and Stavros Petridis and Björn W. Schuller and Maja Pantic},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3011--3... | The large amount of audiovisual content being shared online today has
drawn substantial attention to the prospect of audio-visual self-supervised
learning. Recent works have focused on each of these modalities separately,
while others have attempted to model both simultaneously in a cross-modal
fashion. However, compar... | 2106.09171 | title_snapshot |
rose21_interspeech | End-to-End Audio-Visual Speech Recognition for Overlapping Speech | [
"Richard Rose",
"Olivier Siohan",
"Anshuman Tripathi",
"Otavio Braga"
] | https://www.isca-archive.org/interspeech_2021/rose21_interspeech.html | https://www.isca-archive.org/interspeech_2021/rose21_interspeech.pdf | 10.21437/Interspeech.2021-1621 | 3016-3020 | @inproceedings{rose21_interspeech,
title = {{End-to-End Audio-Visual Speech Recognition for Overlapping Speech}},
author = {Richard Rose and Olivier Siohan and Anshuman Tripathi and Otavio Braga},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3016--3020},
doi = {10.21437/In... | This paper investigates an end-to-end audio-visual (A/V) modeling approach
for transcribing utterances in scenarios where there are overlapping
speech utterances from multiple talkers. It assumes that overlapping
audio signals and video signals in the form of mouth-tracks aligned
with speech are available for overlappi... | null | null |
wu21e_interspeech | Audio-Visual Multi-Talker Speech Recognition in a Cocktail Party | [
"Yifei Wu",
"Chenda Li",
"Song Yang",
"Zhongqin Wu",
"Yanmin Qian"
] | https://www.isca-archive.org/interspeech_2021/wu21e_interspeech.html | https://www.isca-archive.org/interspeech_2021/wu21e_interspeech.pdf | 10.21437/Interspeech.2021-2128 | 3021-3025 | @inproceedings{wu21e_interspeech,
title = {{Audio-Visual Multi-Talker Speech Recognition in a Cocktail Party}},
author = {Yifei Wu and Chenda Li and Song Yang and Zhongqin Wu and Yanmin Qian},
year = {2021},
booktitle = {{Interspeech 2021}},
pages = {3021--3025},
doi = {10.21437/Inters... | Speech from microphones is vulnerable in a complex acoustic environment
due to noise and reverberation, while the cameras are not. Thus, utilizing
the visual modality in the “cocktail party” scenario with
multi-talkers has become a promising and popular approach. In this
paper, we have explored the incorporating of vis... | null | null |
chen21l_interspeech | Ultra Fast Speech Separation Model with Teacher Student Learning | [
"Sanyuan Chen",
"Yu Wu",
"Zhuo Chen",
"Jian Wu",
"Takuya Yoshioka",
"Shujie Liu",
"Jinyu Li",
"Xiangzhan Yu"
] | https://www.isca-archive.org/interspeech_2021/chen21l_interspeech.html | https://www.isca-archive.org/interspeech_2021/chen21l_interspeech.pdf | 10.21437/Interspeech.2021-142 | 3026-3030 | @inproceedings{chen21l_interspeech,
title = {{Ultra Fast Speech Separation Model with Teacher Student Learning}},
author = {Sanyuan Chen and Yu Wu and Zhuo Chen and Jian Wu and Takuya Yoshioka and Shujie Liu and Jinyu Li and Xiangzhan Yu},
year = {2021},
booktitle = {{Interspeech 2021}},
pages ... | Transformer has been successfully applied to speech separation recently
with its strong long-dependency modeling capacity using a self-attention
mechanism. However, Transformer tends to have heavy run-time costs
due to the deep encoder layers, which hinders its deployment on edge
devices. A small Transformer model with... | 2204.12777 | title_snapshot |
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