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In this paper we propose the utterance-level Permutation Invariant Training(uPIT) technique. uPIT is a practically applicable, end-to-end, deep learningbased solution for speaker independent multi-talker speech separation.Specifically, uPIT extends the recently proposed Permutation Invariant Training(PIT) technique with an utterance-level cost function, hence eliminating theneed for solving an additional permutation problem during inference, which isotherwise required by frame-level PIT. We achieve this using Recurrent NeuralNetworks (RNNs) that, during training, minimize the utterance-level separationerror, hence forcing separated frames belonging to the same speaker to bealigned to the same output stream. In practice, this allows RNNs, trained withuPIT, to separate multi-talker mixed speech without any prior knowledge ofsignal duration, number of speakers, speaker identity or gender. We evaluateduPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasksand found that uPIT outperforms techniques based on Non-negative MatrixFactorization (NMF) and Computational Auditory Scene Analysis (CASA), andcompares favorably with Deep Clustering (DPCL) and the Deep Attractor Network(DANet). Furthermore, we found that models trained with uPIT generalize well tounseen speakers and languages. Finally, we found that a single model, trainedwith uPIT, can handle both two-speaker, and three-speaker speech mixtures.

Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks