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Speaker verification has been studied mostly under the single-talkercondition. It is adversely affected in the presence of interference speakers.Inspired by the study on target speaker extraction, e.g., SpEx, we propose aunified speaker verification framework for both single- and multi-talkerspeech, that is able to pay selective auditory attention to the target speaker.This target speaker verification (tSV) framework jointly optimizes a speakerattention module and a speaker representation module via multi-task learning.We study four different target speaker embedding schemes under the tSVframework. The experimental results show that all four target speaker embeddingschemes significantly outperform other competitive solutions for multi-talkerspeech. Notably, the best tSV speaker embedding scheme achieves 76.0% and 55.3%relative improvements over the baseline system on the WSJ0-2mix-extr andLibri2Mix corpora in terms of equal-error-rate for 2-talker speech, while theperformance of tSV for single-talker speech is on par with that of traditionalspeaker verification system, that is trained and evaluated under the samesingle-talker condition.

Target Speaker Verification with Selective Auditory Attention for Single and Multi-talker Speech