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

Rafiqul Islam, Makoto Hiroshige, Yoshikazu Miyanaga, Koji Tochinai

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1816-1819
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume3
Publication statusPublished - 1995
Externally publishedYes
EventProceedings of the 1995 IEEE International Symposium on Circuits and Systems-ISCAS 95. Part 3 (of 3) - Seattle, WA, USA
Duration: Apr 30 1995May 3 1995

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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