Toward streaming ASR with non-autoregressive insertion-based model

Yuya Fujita, Tianzi Wang, Shinji Watanabe, Motoi Omachi

研究成果: Conference contribution

抄録

Neural end-to-end (E2E) models have become a promising technique to realize practical automatic speech recognition (ASR) systems. When realizing such a system, one important issue is the segmentation of audio to deal with streaming input or long recording. After audio segmentation, the ASR model with a small real-time factor (RTF) is preferable because the latency of the system can be faster. Recently, E2E ASR based on non-autoregressive models becomes a promising approach since it can decode an N-length token sequence with less than N iterations. We propose a system to concatenate audio segmentation and non-autoregressive ASR to realize high accuracy and low RTF ASR. As a non-autoregressive ASR, the insertion-based model is used. In addition, instead of concatenating separated models for segmentation and ASR, we introduce a new architecture that realizes audio segmentation and non-autoregressive ASR by a single neural network. Experimental results on Japanese and English dataset show that the method achieved a reasonable trade-off between accuracy and RTF compared with baseline autoregressive Transformer and connectionist temporal classification.

本文言語English
ホスト出版物のタイトル22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
出版社International Speech Communication Association
ページ1426-1430
ページ数5
ISBN(電子版)9781713836902
DOI
出版ステータスPublished - 2021
外部発表はい
イベント22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 - Brno, Czech Republic
継続期間: 2021 8月 302021 9月 3

出版物シリーズ

名前Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2
ISSN(印刷版)2308-457X
ISSN(電子版)1990-9772

Conference

Conference22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
国/地域Czech Republic
CityBrno
Period21/8/3021/9/3

ASJC Scopus subject areas

  • 言語および言語学
  • 人間とコンピュータの相互作用
  • 信号処理
  • ソフトウェア
  • モデリングとシミュレーション

フィンガープリント

「Toward streaming ASR with non-autoregressive insertion-based model」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル