多声源环境下的鲁棒说话人识别

摘要

针对多声源干扰环境下说话人识别系统性能急剧下降的问题，提出一种提取目标语音的前端处理方法，该方法依据独立语音时频域的近似稀疏性，基于目标语音方位信息采用非线性时频掩蔽方法提取目标语音。建立了基于梅尔倒谱系数(MFCC)的高斯混合模型(GMM)说话人识别系统。仿真实验证明，该方法能有效提取目标语音，提高说话人识别系统的鲁棒性。该文多声源干扰仿真实验条件下，说话人识别系统的识别率平均提高了25%左右。%The Speaker Recognition System is significantly affected by the Multi-Sound sources problem. In order to overcome this problem, a target sound extraction algorithm named time-frequency masking is proposed. The proposed algorithm is based on the sound source azimuth information and the approximate sparse nature of sound. A Mel-frequency cepstral coefficient (MFCC) based Gaussian mixture model (GMM) speaker recognition system is presented to improve the recognition robustness. The proposed algorithm has been tested on the simulated data through a number of experiments which shows the efficiency and robustness of the proposed algorithm. In the Multi-Sound sources environment, the recognition rate of the proposed algorithm can be improved by about 25%.