SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition

Patrick K. O'Neill*, Vitaly Lavrukhin, Somshubra Majumdar, Vahid Noroozi, Yuekai Zhang, Oleksii Kuchaiev, Jagadeesh Balam, Yuliya Dovzhenko, Keenan Freyberg, Michael D. Shulman, Boris Ginsburg, Shinji Watanabe, Georg Kucsko

*この研究の対応する著者

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models. This adds complexity and limits performance, as many formatting tasks benefit from semantic information present in the acoustic signal but absent in transcription. Here we propose a new STT task: endto-end neural transcription with fully formatted text for target labels. We present baseline Conformer-based models trained on a corpus of 5,000 hours of professionally transcribed earnings calls, achieving a CER of 1.7. As a contribution to the STT research community, we release the corpus free for noncommercial use.

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1081-1085
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

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