TONE RECOGNITION OF CONTINUOUS MANDARIN SPEECH BASED ON HIDDEN MARKOV MODEL

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Abstract
In this paper, several tone recognition schemes for continuous Mandarin speech are discussed. First, an SCHMM is used to model the acoustic features of a syllable for tone discrimination. Parameters extracted from the F0 and energy contours of the syllable by discrete Legendre orthonormal transform are used as the recognition features. Then, a scheme using two-layer network is proposed to cope with the difficulty resulting from the declination effect on the F0 contour of the declarative sentential utterance. The declination effect is modeled by a sentence-level HMM on the upper layer and the acoustic features of each tone are modeled by a state-dependent SCHMM on the lower layer. Lastly, the coarticulation effect coming from neighboring syllables is considered in the scheme using context-dependent model. Performance of these recognition schemes was examined by simulations. A recognition rate of 86.34% was achieved.

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