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

摘要： 矢量量化(VQ)方法是文本无关说话人识别中广泛应用的建模方法之一，它的主要问题是码本设计问题。语音特征参数是高维数据，样本分布复杂，因此码本设计的难度也很大，传统的LBG算法只能获得局部最优的码本。提出一种VQ码本设计的新方法，将小生境技术与K-均值算法融入到免疫算法训练过程中，形成混合免疫算法，采用针对高维数据聚类的改进变异算子，降低了随机变异的盲目性，增强群体的全局及局部搜索能力，同时通过接种疫苗提高算法的收敛速度。说话人识别实验表明，与传统LBG和基于混合遗传算法的VQ码本设计方法相比，该方法可以得到更优的模型参数，使得系统的识别率进一步提高。

关键词: 说话人识别, 免疫算法, 矢量量化, 遗传算法