Discriminant methods for improving the robustness of Mandarin syllables recognition based upon hidden Markov model

Two types of hidden Markov modeling (HMM), discrete-type (vector quantization (VQ)-based) and mixture density, are used to overcome part of the problem of Mandarin syllable recognition. In the VQ-based HMM part, a two-stage process is used to generate a VQ codebook in the hope of avoiding the possible domination of the vowel signal vectors on the codewords in vector space. A polyobservation sequence approach is adopted to minimize the quantization error. In the mixture density approach, the context-dependent models are incorporated in the classification of different buffers in the memory. The 408 syllables are divided into 154 buffers, 115 for consonants and 39 for vowels. In this way a buffer with tied probabilities may be shared by several different syllables. Hence, the bias between the confusing syllables with the same vowel part is expected to be eliminated by tied probability techniques. Experimental results show that improvement can be achieved with both types of HMM