Genetic algorithm on speech recognition by using DHMM

This paper uses genetic algorithms to train a codebook for the modeling of Discrete Hidden Markov Model (DHMM) applied to speech recognition. The GA-trained DHMM is then used to increase the recognition rate for Mandarin speeches. Vector quantization based on a codebook is a fundamental process to recognize the speech signal by DHMM. A codebook will be first trained by genetic algorithms through Mandarin speech features. The speech features are then quantized based on the trained codebook. Subsequently, the quantized speech features are statistically used to train the model of DHMM for speech recognition. All the speech features to be recognized should go through the codebook before being fed into the DHMM model for recognition. Experimental results show that the speech recognition rate can be improved by using genetic algorithms to train the model of DHMM.