关键词: 说话人识别, 多类型语音特征, 链式智能体遗传算法, 伽马通滤波器倒谱系数（GFCC）, 梅尔频率倒谱系数（MFCC）, 线性预测倒谱系数（LPCC）

Abstract: Speech feature extraction based on feature selection is a very effective method for speaker recognition. However, the optimal speech features have also changed. Therefore, this paper proposes a kind of four kinds of speech feature wrapper selection framework algorithm（FSF-WrGAF）. The algorithm extracts four kinds of speech features, and conducts dynamic wrapper feature selection by Chainlike Agent Genetic Algorithm（CAGA） and Gaussian Mixture Model-Universal Background Model（GMM-UBM）, thereby obtaining high recognition accuracy. Several algorithms are compared in the experiment part. Experimental results show that the FSF-WrGAF algorithm can obtain apparent improvement in terms of accuracy, equal error rate and detection cost compared with some other algorithms.

Key words: speaker recognition, multiple voice features types, chain-like agent genetic algorithm, Gammatone Frequency Cepstrum Coefficient（GFCC）, Mel Frequency Cepstrum Coefficient（MFCC）, Linear Prediction Cepstrum Coefficient（LPCC）