Target Speaker Extraction for Multi-Talker Speaker Verification

The performance of speaker verification degrades significantly when the test speech is corrupted by interference from non-target speakers. Speaker diarization separates speakers well only if the speakers are not overlapped. However, if multiple talkers speak at the same time, we need a technique to separate the speech in the spectral domain. In this paper, we study a way to extract the target speaker’s speech from an overlapped multi-talker speech. Specifically, given some reference speech samples from the target speaker, the target speaker’s speech is firstly extracted from the overlapped multi-talker speech, then the extracted speech is processed in the speaker verification system. Experimental results show that the proposed approach significantly improves the performance of overlapped multi-talker speaker verification and achieves 64.4% relative EER reduction over the zero-effort baseline.