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.
|ジャーナル||Systems and Computers in Japan|
|出版物ステータス||Published - 1999 4 1|
ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Hardware and Architecture
- Computational Theory and Mathematics