Keyword Recognition Based on MFCC

Author:

Yang Sha1,Hu Tian2,Zhang Yun Lu3

Affiliation:

1. Hankou University

2. Wuhan Digital Engineering Institue

3. Synopsys Inc.

Abstract

After about 50 years of development, speech recognition technology has been able to achieve large vocabulary, non-specific human continuous speech recognition system. On account of Chinese pronunciation features, we research the small vocabulary, non-specific Chinese speech recognition based on continuous Hidden Markov Model approach. With comparing the datasets of VQ/DTW, VQ/DHMM, CHMM state-1 recognition algorithm and CHMM state-2 recognition algorithm, the results of our experiment show that: (1) CHMM state-2 branch method performs primely in reduction of the recognition time; and (2) the recognition accuracy is improved eventually.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Reference9 articles.

1. Myers C S, Rabiner L R, Rosenberg A E. An Investigation of the Use of Dynamic Time Warping for Word Spotting and Connected Word Recognition. ICASSP 80, pp.173-177.

2. Higgins Alan L, Wohlord Robert E. Keyword Recognition Using Template Concatenation. ICASSP85, 1985(3) pp.233-1236.

3. Wilson J G, Lee C H, Rabiner L R. Application of Hidden Markov Model for Recognation of a Limited Set of Words in Unconstrained Speech. ICASSP85, 1989(3) pp.173-177.

4. Wilson Jay G, Rabiner Lawrence R, etal. Automatic Recognation of Key-words in Unconstrained Speech Using Hidden Markov Model. on ASSP, 1990, 38(11) p.1980-(1978).

5. A. Martin,GDoddington,T. Kamm,M. ordowski,andM. Przyboeki. The DET Curve in Assessment of Detection Task Performance. In Proc. Eurospeeeh. Rhodes,Greece. 1997, pp.1895-1898.

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