Phoneme boundary estimation using bidirectional recurrent neural networks and its applications

Toshiaki Fukada, Mike Schuster, Yoshinori Sagisaka

研究成果: Article

8 引用 (Scopus)

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This paper describes a phoneme boundary estimation method based on bidirectional recurrent neural networks (BRNNs). Experimental results showed that the proposed method could estimate segment boundaries significantly better than an HMM or a multilayer perceptron-based method. Furthermore, we incorporated the BRNN-based segment boundary estimator into the HMM-based and segment model-based recognition systems. As a result, we confirmed that (1) BRNN outputs were effective for improving the recognition rate and reducing computational time in an HMM-based recognition system and (2) segment lattices obtained by the proposed methods dramatically reduce the computational complexity of segment model-based recognition.

元の言語English
ページ(範囲)20-30
ページ数11
ジャーナルSystems and Computers in Japan
30
発行部数4
DOI
出版物ステータスPublished - 1999 4 1
外部発表Yes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture
  • Computational Theory and Mathematics

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