CAJ | 학술논문

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。
침대다성원간우배경하설화인식별계통성능급극하강적문제，제출일충제취목표어음적전단처리방법，해방법의거독립어음시빈역적근사희소성，기우목표어음방위신식채용비선성시빈엄폐방법제취목표어음。건립료기우매이도보계수(MFCC)적고사혼합모형(GMM)설화인식별계통。방진실험증명，해방법능유효제취목표어음，제고설화인식별계통적로봉성。해문다성원간우방진실험조건하，설화인식별계통적식별솔평균제고료25%좌우。
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%.