Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU134179" target="_blank" >RIV/00216305:26230/19:PU134179 - isvavai.cz</a>
Result on the web
<a href="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3167.pdf" target="_blank" >https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3167.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21437/Interspeech.2019-3167" target="_blank" >10.21437/Interspeech.2019-3167</a>
Alternative languages
Result language
angličtina
Original language name
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text
Original language description
Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance. For this reason, there has been a recent surge in interest for unsupervised and semi-supervised training in such models. This work builds upon recent results showing notable improvements in semi-supervised training using cycle-consistency and related techniques. Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with Text-to-Speech (TTS) models. In particular, this work proposes a new semi-supervised loss combining an end-to-end differentiable ASR!TTS loss with TTS!ASR loss. The method is able to leverage both unpaired speech and text data to outperform recently proposed related techniques in terms of %WER. We provide extensive results analyzing the impact of data quantity and speech and text modalities and show consistent gains across WSJ and Librispeech corpora. Our code is provided in ESPnet to reproduce the experiments.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of Interspeech
ISBN
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ISSN
1990-9772
e-ISSN
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Number of pages
5
Pages from-to
3790-3794
Publisher name
International Speech Communication Association
Place of publication
Graz
Event location
INTERSPEECH 2019
Event date
Sep 15, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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