Multi-speaker recognition based on MFCC and motionintensity clustering initialization
基于MFCC和运动强度聚类初始化的多说话人识别

Aiming at the problem of conventional initialization methods performed on audio feature of multiple speakers clustering with poor accuracy, this paper proposed a new method visual-based feature. The method used motion intensity feature with each time-frame of visual information to find initial speaker cluster during the process of clustering initialization, and promoted the purity of speaker initial cluster effectively. Finally, applied this method to Gaussian mixture model GMM multi-speaker recognition system. And the experimental results show that, across the entire meeting set, this proposed new method achieved consistent improvements over other methods, and compared to linear initialization it makes the error recognition of system been reduced by 19. 436% on average; 16. 618% to the improved linear initialization.