Abstract: At present, the speech tracking quality will be seriously reduced under the condition of speaker interference, that is, mixed speech signals of multiple speakers in a speech segment. Aiming at this situation, a speech tracking algorithm based on cluster analysis and speaker recognition is proposed. Firstly, the improved clustering analysis method is used for speech separation. Specifically, it includes caching the center of mass and lowering the sampling rate in K-means clustering, and introducing regular terms into embedding feature space. Secondly, the GMM-UBM speaker model is used for speech tracking. The experimental results show that the improved cluster analysis method can effectively improve the real-time performance of the algorithm and the quality of speech separation, the GMM-UBM model has an 84% recognition rate in 3 s speech test.

Key words: single channel speech track, intelligent speech, clustering analysis, Gaussian mixture model, LSTM