Blind speech separation of moving speakers in real reverberant environments

In this paper we present a new on-line Blind Signal Separation method capable to separate convolutive speech signals of moving speakers in highly reverberant rooms. The separation network used is a recurrent network which performs separation of convolutive speech mixtures in the time domain, without any prior knowledge of the propagation media, based on the Maximum Likelihood Estimation (MLE) principle. The proposed method proved to be able to improve significantly (more than 10% in all adverse mixing situations) the performance of a continuous phoneme-based speech recognition system and therefore can be used as a front-end to separate simultaneous speech of speakers who are moving in arbitrary directions in reverberant rooms.