Summary
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Response Time Reduction of Speech Recognizers Using Single Gaussians Sangbae JEONG Hoirin KIM Minsoo HAHN Publication IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.5 pp.868-871 Publication Date: 2007/05/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.5.868 Print ISSN: 0916-8532 Type of Manuscript: LETTER Category: Speech and Hearing Keyword: speech recognition, fast likelihood computation, Full Text: PDF(194.9KB)>>
Summary: In this paper, we propose a useful algorithm that can be applied to reduce the response time of speech recognizers based on HMM's. In our algorithm, to reduce the response time, promising HMM states are selected by single Gaussians. In speech recognition, HMM state likelihoods are evaluated by the corresponding single Gaussians first, and then likelihoods by original full Gaussians are computed and replaced only for the HMM states having relatively large likelihoods. By doing so, we can reduce the pattern-matching time for speech recognition significantly without any noticeable loss of the recognition rate. In addition, we cluster the single Gaussians into groups by measuring the distance between Gaussians. Therefore, we can reduce the extra memory much more. In our 10,000 word Korean POI (point-of-interest) recognition task, our proposed algorithm shows 35.57% reduction of the response time in comparison with that of the baseline system at the cost of 10% degradation of the WER. | ||||||||||
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