A Comparative Study on Transformer vs RNN in Speech Applications

Shigeki Karita, Xiaofei Wang, Shinji Watanabe, Takenori Yoshimura, Wangyou Zhang, Nanxin Chen, Tomoki Hayashi, Takaaki Hori, Hirofumi Inaguma, Ziyan Jiang, Masao Someki, Nelson Enrique Yalta Soplin, Ryuichi Yamamoto

研究成果: Conference contribution

133 被引用数 (Scopus)

抄録

Sequence-To-sequence models have been widely used in end-To-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-To-speech (TTS). This paper focuses on an emergent sequence-To-sequence model called Transformer, which achieves state-of-The-Art performance in neural machine translation and other natural language processing applications. We undertook intensive studies in which we experimentally compared and analyzed Transformer and conventional recurrent neural networks (RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS benchmarks. Our experiments revealed various training tips and significant performance benefits obtained with Transformer for each task including the surprising superiority of Transformer in 13/15 ASR benchmarks in comparison with RNN. We are preparing to release Kaldi-style reproducible recipes using open source and publicly available datasets for all the ASR, ST, and TTS tasks for the community to succeed our exciting outcomes.

本文言語English
ホスト出版物のタイトル2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ449-456
ページ数8
ISBN(電子版)9781728103068
DOI
出版ステータスPublished - 2019 12
外部発表はい
イベント2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Singapore, Singapore
継続期間: 2019 12 152019 12 18

出版物シリーズ

名前2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019 - Proceedings

Conference

Conference2019 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019
国/地域Singapore
CitySingapore
Period19/12/1519/12/18

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 信号処理
  • 言語学および言語
  • 通信

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