LARGE VOCABULARY WORD RECOGNITION SYSTEM USING RULE-BASED NETWORK REPRESENTATION OF ACOUSTIC CHARACTERISTIC VARIATIONS.

Satoru Hayamizu*, Kazuyo Tanaka, Kozo Ohta

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

A method to represent, acquire and implement acoustic-phonetic knowledge for large vocabulary word recognition is described. The knowledge is represented using networks of acoustic-phonetic segments, acquired from a speech database and stored as rules which are used to generate those networks. Different standard patterns of segments are used for each VCV or CVC environment. Network matching and segment clustering are used to implement an efficient recognition procedure. Experiments on speaker-independent isolated-word recognition were conducted with 10 male speakers' utterances. The word recognition accuracy was 99. 4% for 53 city names and 96. 0% for 492 words of the phonetically balanced word set, respectively.

Original languageEnglish
Pages (from-to)211-214
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publication statusPublished - 1988
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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