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

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.