Text-independent speaker identification based on genetic algorithm

To solve the issues that K-mean algorithm is easy to fall into a local optimal result and the design of best codebook greatly depends on selection of initial codebook in the Vector Quantization(VQ)system of speaker identification,the algorithm GA-K about codebook design is proposed by combining Genetic Algorithm(GA)with VQ based on nonparametric model.The algorithm uses the global optimization function of GA to obtain the best VQ codebook and avoids converging local optimal result of LBG algorithm.Through the parameters of GA,the global optimal codebooks are found out in the training vectors combined with the fast convergence of K-mean algorithm.The experiments show that GA-K algorithm is more effective than LBG algorithm and it can well deal with the relations between convergence and recognition rate.