Continuous speech recognition by context-dependent phonetic HMM and an efficient algorithm for finding N-Best sentence hypotheses

Authors:
Katunobu Itou;Satoru Hayamizu;Hozumi Tanaka
Affiliations:
Tokyo Institute of Technology, Tokyo, Japan;Electrotechnical Laboratory, Tsukuba, Ibaraki, Japan;Tokyo Institute of Technology, Tokyo, Japan
Venue:
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Year:
1992

Citing 1
Cited 1

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ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference

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CHI '93 Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems

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Abstract

In this paper, a continuous speech recognition system, "niNja" (Natural language INterface in JApanese), is presented. Efficient search algorithms are proposed to get high accuracy and to reduce the required computations. First, an LR parsing algorithm with context-dependent phone models is proposed. Second, scores of the same phone models in different hypotheses at the phone-level are represented by the single score of the best hypothesis. The system is tested for the task with 113 word vocabulary, word perplexity 4.1. It produces sentence accuracy of 97.3% for the 10 open speakers's 110 sentences and the error reduction is as much as 77% comparing with the case using context independent phone models.