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We propose an end-to-end trainable approach to single-channel speechseparation with unknown number of speakers. Our approach extends the MulCatsource separation backbone with additional output heads: a count-head to inferthe number of speakers, and decoder-heads for reconstructing the originalsignals. Beyond the model, we also propose a metric on how to evaluate sourceseparation with variable number of speakers. Specifically, we cleared up theissue on how to evaluate the quality when the ground-truth hasmore or lessspeakers than the ones predicted by the model. We evaluate our approach on theWSJ0-mix datasets, with mixtures up to five speakers. We demonstrate that ourapproach outperforms state-of-the-art in counting the number of speakers andremains competitive in quality of reconstructed signals.

Multi-Decoder DPRNN: High Accuracy Source Counting and Separation