Single-channel Speaker Separation Based on Sub-spectrum GMM and Bayesian Theory

摘要：

The problem of single-channel speaker separation attempts to extract the speech signal uttered by the speaker of interest from one channel signals containing a mixture of acoustic signals.Most of current techniques failed to eliminate the interfering signal completely.In this paper,we present a new approach to solve this problem.Its an iterative separation approach based on sub-spectrum GMM and Bayesian theory.First,we obtain sub-spectrum GMM models for each speaker in the training phase.Then,separated speech signals are estimated based on Bayesian model given the mixture.Finally,an iterative separation process is used to separate out the speech signal of the speaker of interest from the mixture.Simulation results exhibit a high level of separating performance.

作者: Haiyan Guo Xi Shao Zhen Yang

作者单位: Institute of Signal Processing and Transmission,Nanjing University of Posts and Telecommunications,N College of Telecommunications & Information Engineering,Nanjing University of Posts and Telecommunic

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）