A large vocabulary word recognition system using rule-based network representation of acoustic characteristic variations

The authors describe a method to represent, acquire and implement acoustic-phonetic knowledge for large vocabulary word recognition. 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 done to implement an efficient recognition procedure. Experiments of 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. These results show the effectiveness of the method