A Study of Transducer Based End-to-End ASR with ESPnet: Architecture, Auxiliary Loss and Decoding Strategies

Florian Boyer, Yusuke Shinohara, Takaaki Ishii, Hirofumi Inaguma, Shinji Watanabe

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this study, we present recent developments of models trained with the RNN-T loss in ESPnet. It involves the use of various archi-tectures such as recently proposed Conformer, multi-task learning with different auxiliary criteria and multiple decoding strategies, in-cluding our own proposition. Through experiments and benchmarks, we show that our proposed systems can be competitive against other state-of-art systems on well-known datasets such as LibriSpeech and AISHELL-1. Additionally, we demonstrate that these models are promising against other already implemented systems in ESPnet in regards to both performance and decoding speed, enabling the pos-sibility to have powerful systems for a streaming task. With these additions, we hope to expand the usefulness of the ESPnet toolkit for the research community and also give tools for the ASR industry to deploy our systems in realistic and production environments.

本文言語English
ホスト出版物のタイトル2021 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ16-23
ページ数8
ISBN(電子版)9781665437394
DOI
出版ステータスPublished - 2021
外部発表はい
イベント2021 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021 - Cartagena, Colombia
継続期間: 2021 12月 132021 12月 17

出版物シリーズ

名前2021 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021 - Proceedings

Conference

Conference2021 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021
国/地域Colombia
CityCartagena
Period21/12/1321/12/17

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • 信号処理
  • 言語学および言語

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