Phoneme recognition using modified TDNN and a self-organizing clustering network

Rafiqul Islam, Makoto Hiroshige, Yoshikazu Miyanaga, Koji Tochinai

研究成果: Contribution to journalConference article査読

1 被引用数 (Scopus)

抄録

This paper presents a new approach to phoneme recognition system. A modified Time-delay Neural Network (TDNN) based on similarity vectors of clustering node information is developed for this purpose. The speech data have been analyzed first by time varying ARMA-D model to have better response of its time varying characteristics. For the generation of the similarity vectors of the clustering nodes, Self-Organizing Clustering process is used. To study the performance of this system, the speaker-independent recognition of the voiced explosive(stop) consonants /b,d,g/ in varying phonetic contexts is taken as the initial recognition task. This system gives a recognition rate for the stop consonants of about 84.3% for speaker independent speech data. For all these experiments, Japanese speech data is used supplied by ATR, Japan. The time taken for the training and recognition by the system can be considered reasonable.

本文言語英語
ページ(範囲)1816-1819
ページ数4
ジャーナルProceedings - IEEE International Symposium on Circuits and Systems
3
出版ステータス出版済み - 1995
外部発表はい
イベントProceedings of the 1995 IEEE International Symposium on Circuits and Systems-ISCAS 95. Part 3 (of 3) - Seattle, WA, USA
継続期間: 4 30 19955 3 1995

All Science Journal Classification (ASJC) codes

  • 電子工学および電気工学

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