关键词: 说话人识别, 免疫算法, 矢量量化, 遗传算法

Abstract: Vector Quantization(VQ) is one of the popular codebook design methods for text-independent speaker identification. The key problem of VQ is the design of codebook. Speech feature parameters have complex distribution with high dimensions. Therefore, we have great difficulty in designing codebook. The traditional LBG algorithm yields only local optimal codebook. In this paper, a new method of codebook design was proposed, named as hybrid immune algorithm. It utilized the niche technology and K-means algorithm in the immune algorithm train step. It adopted improved mutation operator for data clustering with high dimension, reduced the blindness of stochastic mutation, so as to improve the local and global searching capability and increase the convergent speed by vaccination. Experiment for text-independent speaker identification shows that this method can obtain more optimum VQ parameters and better results than the LBG and hybrid genetic algorithm.

Key words: speaker identification, immune algorithm, Vector Quantization, genetic algorithm