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We study permutation invariant training (PIT), which targets at thepermutation ambiguity problem for speaker independent source separation models.We extend two state-of-the-art PIT strategies. First, we look at the two-stagespeaker separation and tracking algorithm based on frame level PIT (tPIT) andclustering, which was originally proposed for the STFT domain, and we adapt itto work with waveforms and over a learned latent space. Further, we propose anefficient clustering loss scalable to waveform models. Second, we extend arecently proposed auxiliary speaker-ID loss with a deep feature loss based on"problem agnostic speech features", to reduce the local permutation errors madeby the utterance level PIT (uPIT). Our results show that the proposedextensions help reducing permutation ambiguity. However, we also note that thestudied STFT-based models are more effective at reducing permutation errorsthan waveform-based models, a perspective overlooked in recent studies.

On permutation invariant training for speech source separation