K-L divergence based model clustering method for fast speaker identification

With the increase of enrolled speakers and audio data to be recognized, the conventional speaker identification methods can not meet the real-time demand for internet application environment. A K-L divergence based speaker model clustering method is proposed to construct a hierarchical identification system, which remarkably improves the recognition efficiency. Moreover, the confidence measure using class-level identification information is also investigated to effectively exclude out-of-set speaker as early as possible. The experimental results show the proposed method averagely increases the identification speed by 3.2 times while the error rate of closed-set identification only increases about 0.9% compared with the conventional method. The open-set identification can be speeded up by using class-level confidence measure and a relatively 5.1% error rate reduction can be achieved on out-of-set speakers identification while keeping the identification performance of in-set speakers unchanged.