Detail publikačního výsledku

Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text

BASKAR, M.; WATANABE, S.; ASTUDILLO, R.; HORI, T.; BURGET, L.; ČERNOCKÝ, J.

Originální název

Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text

Anglický název

Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Sequence-to-sequence automatic speech recognition (ASR)models require large quantities of data to attain highperformance. For this reason, there has been a recent surgein interest for unsupervised and semi-supervised training insuch models. This work builds upon recent results showingnotable improvements in semi-supervised training usingcycle-consistency and related techniques. Such techniquesderive training procedures and losses able to leverage unpairedspeech and/or text data by combining ASR with Text-to-Speech(TTS) models. In particular, this work proposes a newsemi-supervised loss combining an end-to-end differentiableASR!TTS loss with TTS!ASR loss. The method is ableto leverage both unpaired speech and text data to outperformrecently proposed related techniques in terms of %WER. Weprovide extensive results analyzing the impact of data quantityand speech and text modalities and show consistent gains acrossWSJ and Librispeech corpora. Our code is provided in ESPnetto reproduce the experiments.

Anglický abstrakt

Sequence-to-sequence automatic speech recognition (ASR)models require large quantities of data to attain highperformance. For this reason, there has been a recent surgein interest for unsupervised and semi-supervised training insuch models. This work builds upon recent results showingnotable improvements in semi-supervised training usingcycle-consistency and related techniques. Such techniquesderive training procedures and losses able to leverage unpairedspeech and/or text data by combining ASR with Text-to-Speech(TTS) models. In particular, this work proposes a newsemi-supervised loss combining an end-to-end differentiableASR!TTS loss with TTS!ASR loss. The method is ableto leverage both unpaired speech and text data to outperformrecently proposed related techniques in terms of %WER. Weprovide extensive results analyzing the impact of data quantityand speech and text modalities and show consistent gains acrossWSJ and Librispeech corpora. Our code is provided in ESPnetto reproduce the experiments.

Klíčová slova

Sequence-to-sequence, end-to-end, ASR, TTS,semi-supervised, unsupervised, cycle consistency

Klíčová slova v angličtině

Sequence-to-sequence, end-to-end, ASR, TTS,semi-supervised, unsupervised, cycle consistency

Autoři

BASKAR, M.; WATANABE, S.; ASTUDILLO, R.; HORI, T.; BURGET, L.; ČERNOCKÝ, J.

Rok RIV

2020

Vydáno

15.09.2019

Nakladatel

International Speech Communication Association

Místo

Graz

Kniha

Proceedings of Interspeech

ISSN

1990-9772

Periodikum

Proceedings of Interspeech

Svazek

2019

Číslo

9

Stát

Francouzská republika

Strany od

3790

Strany do

3794

Strany počet

5

URL

https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3167.pdf

BibTex

@inproceedings{BUT159996,
  author="BASKAR, M. and WATANABE, S. and ASTUDILLO, R. and HORI, T. and BURGET, L. and ČERNOCKÝ, J.",
  title="Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text",
  booktitle="Proceedings of Interspeech",
  year="2019",
  journal="Proceedings of Interspeech",
  volume="2019",
  number="9",
  pages="3790--3794",
  publisher="International Speech Communication Association",
  address="Graz",
  doi="10.21437/Interspeech.2019-3167",
  issn="1990-9772",
  url="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3167.pdf"
}

Dokumenty

baskar_is2019_193167