5th International Conference on Spoken Language Processing

Sydney, Australia
November 30 - December 4, 1998

Speech Separation Based on the GMM PDF Estimation

Xiao Yu, Guangrui Hu

Shanghai Jiao Tong University, China

In this paper, the speech separation task will be regarded as a convolutive mixture Blind Source Separation (BSS) problem. The Maximum Entropy (ME) algorithm, the Minimum Mutual Information (MMI) algorithm and the Maximum Likelihood (ML) algorithm are main approaches of the algorithms solving the BSS problem. The relationship of these three algorithms has been analyzed in this paper. Based on the feedback network architecture, a new speech separation algorithm is proposed by using the Gaussian Mixture Model (GMM) pdf estimation in this paper. From the computer simulation results, it can be concluded that the proposed algorithm can get faster convergence rate and lower output Mean Square Error than the conventional ME algorithm.

Full Paper

Bibliographic reference.  Yu, Xiao / Hu, Guangrui (1998): "Speech separation based on the GMM PDF estimation", In ICSLP-1998, paper 0286.