Filler prediction based on bidirectional LSTM for generation of natural response of spoken dialog

Yoshihiro Yamazaki, Yuya Chiba, Takashi Nose, Akinori Ito

研究成果: Conference contribution

抄録

Most of the conventional response generation models do not generate speech disfluencies including fillers, because they are trained from a written language corpus. It is necessary to insert fillers to written sentences for training a response generation model for the spoken language. In this paper, we proposed the filler prediction model based on bidirectional LSTM (BLSTM). This approach can consider a whole utterance and model both positions and kinds of fillers simultaneously. The experiments showed that the proposed method surpasses the conventional approach in terms of the prediction accuracy.

本文言語English
ホスト出版物のタイトル2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ360-361
ページ数2
ISBN(電子版)9781728198026
DOI
出版ステータスPublished - 2020 10 13
イベント9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
継続期間: 2020 10 132020 10 16

出版物シリーズ

名前2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
国/地域Japan
CityKobe
Period20/10/1320/10/16

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学
  • メディア記述
  • 器械工学
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識

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