A continuous speech recognition system based on a two-level grammar approach
SHINYA MATSUNAGA, Shigeki Sagayama, Seiha Homma, Sadaoki Furui · International Conference on Acoustics, Speech, and Signal Processing · 2002
A Japanese continuous-speech recognition system is described which is based on phonetic hidden Markov models (HMMs) combined with two levels of grammatical representations: an intraphase transition network grammar and an interphase dependency grammar. A joint score, combining acoustic likelihood and linguistic certainty factors derived from phonetic HMMs and two levels of grammar, is maximized to obtain the optimal recognition results of sentences. Two efficient algorithms, bidirectional network parsing and breadth-first dependency parsing, are deviced to optimize the joint score globally. The system attains a phrase recognition rate of 80.8% with the intraphase parser only and 86.8% with both the intraphase and interphase parsers, where the perplexity of the phrase syntax is 40. This result shows the effectiveness of the two-level grammar approach.>