A genetic algorithm is used to train the fuzzy membership function of a fuzzy codebook for the modeling of Discrete Hidden Markov Model (DHMM) applied to Mandarin speech recognition. Vector quantization for a speech feature based on a codebook is a fundamental process to recognize the speech signal by DHMM. A codebook with fuzzy membership functions corresponding to each vector in the codebook will be first trained by genetic algorithms (GAs) through speech features. The trained fuzzy codebook is then used to quantize the speech features. Subsequently, the quantized speech statistical features are used to model the DHMM for each speech. Besides, all the speech features to be recognized will go through the fuzzy codebook for quantization before being fed into the DHMM model for recognition. Experimental results show that both the speech recognition rate and computation time for recognition can be improved by the proposed strategy.
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