Multi-head decoder for end-to-end speech recognition

Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Kazuya Takeda

研究成果: Conference article査読

2 被引用数 (Scopus)

抄録

This paper presents a new network architecture called multi-head decoder for end-to-end speech recognition as an extension of a multi-head attention model. In the multi-head attention model, multiple attentions are calculated, and then, they are integrated into a single attention. On the other hand, instead of the integration in the attention level, our proposed method uses multiple decoders for each attention and integrates their outputs to generate a final output. Furthermore, in order to make each head to capture the different modalities, different attention functions are used for each head, leading to the improvement of the recognition performance with an ensemble effect. To evaluate the effectiveness of our proposed method, we conduct an experimental evaluation using Corpus of Spontaneous Japanese. Experimental results demonstrate that our proposed method outperforms the conventional methods such as location-based and multi-head attention models, and that it can capture different speech/linguistic contexts within the attention-based encoder-decoder framework.

本文言語English
ページ(範囲)801-805
ページ数5
ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2018-September
DOI
出版ステータスPublished - 2018
外部発表はい
イベント19th Annual Conference of the International Speech Communication, INTERSPEECH 2018 - Hyderabad, India
継続期間: 2018 9 22018 9 6

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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