A segment-based speech recognition system for isolated Mandarin syllables

A segment-based speech recognition scheme is proposed. The basic idea is to explicitly model the correlations between successive frames of an acoustic segment by using features representing the contours of spectral parameters. These segmental features are several lower-order coefficients of discrete orthonormal polynomial expansions. The performance of the proposed scheme was examined by simulations on multi-speaker speech recognition for all 408 highly confusing first-tone Mandarin syllables. A recognition rate of 77.4% was achieved for the case, using five 6-segment reference templates per syllable. This is 13.0% and 6.6% higher than those obtained by a conventional dynamic time warping (DTW) method and a conventional hidden Markov model (CHMM) method, respectively.<
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