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The common target speech separation directly estimate the target source,ignoring the interrelationship between different speakers at each frame. Wepropose a multiple-target speech separation model (MTSS) to simultaneouslyextract each speaker's voice from the mixed speech rather than just optimallyestimating the target source. Moreover, we propose a speaker diarization (SD)aware MTSS system (SD-MTSS), which consists of a SD module and MTSS module. Byexploiting the TSVAD decision and the estimated mask, our SD-MTSS model canextract the speech signal of each speaker concurrently in a conversationalrecording without additional enrollment audio in advance. Experimental resultsshow that our MTSS model achieves 1.38dB SDR, 1.34dB SI-SDR, and 0.13 PESQimprovements over the baseline on the WSJ0-2mix-extr dataset, respectively. TheSD-MTSS system makes 19.2% relative speaker dependent character error rate(CER) reduction on the Alimeeting dataset.

Simultaneous Speech Extraction for Multiple Target Speakers under the Meeting Scenarios