Continuous Speech Recognition Using a Dependency Grammar and Phoneme-Based HMMs

Sho-ichi MATSUNAGA  Shigeru HOMMA  Shigeki SAGAYAMA  Sadaoki FURUI  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E74-A   No.7   pp.1826-1833
Publication Date: 1991/07/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Issue on Continuous Speech Recognition and Understanding)
Category: Continuous Speech Recognition
Keyword: 


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Summary: 
This paper describes two Japanese continuous speech recognition systems (system-1 and system-2) based on phoneme-based HMMs and a two-level grammar approach. Two grammars are an intra-phrase transition network grammar for phrase recognition, and an inter-phrase dependency grammar for sentence recognition. A joint score, combining acoustic likelihood and linguistic certainty factors derived from phonemebased HMMs and dependency rules, is maximized to obtain the best sentence recognition results. System-1 is tuned for sentences uttered phrase-by-phrase and system-2 is tuned for sentence utterances, to make the amount of computation practical. In system-1, two efficient parsing algorithms are used for each grammar. They are a bi-directional network parser and a breadth-first dependency parser. With the phrase-network parser, input phrase utterances are parsed bi-directionally both left-to-right and right-to-left, and optimal Viterbi paths are found along which the accumulated phonetic likelihood is maximized. The dependency parser utilizes efficient breadth-first search and beam search algorithms. For system-2, we have extended the dependency analysis algorithm for sentence utterances, using a technique for detecting most-likely multi-phrase candidates based on the Viterbi phrase alignment. Where the perplexity of the phrase syntax is 40, system-1 and system-2 increase phrase recognition performance in the sentence by approximately 6% and 14%, showing the effectiveness of semantic dependency analysis.