Co-channel speaker segment separation

Abstract

A novel approach to co-channel speaker separation is presented here. The technique uses the statistical properties of combinations of high Target-to-Interferer Ratio (TIR) speech segments, which were extracted from a 0 dB overall TIR co-channel utterance. The problem is broken down into making three simpler decisions. First, closed-set speaker identification technology is used on combinations of high TIR speech segments to determine which speakers are generating the co-channel speech. Next, the proportion of segments belonging to each speaker is estimated using a bimodal model. Lastly, a maximum likelihood decision is made as to which two combinations of segments best represent the two speakers. Using this approach at least one of the speakers could readily be identified when the speaker contributed a segment that was 160 ms or more in length. Once the speakers were determined, greater than 90% reliable speaker separation was obtained.