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Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation
We propose a novel deep learning model, which supports permutation invariant
training (PIT), for speaker independent multi-talker speech separation,
commonly known as the cocktail-party problem. Different from most of the prior
arts that treat speech separation as a multi-class regression problem and the
deep clustering technique that considers it a segmentation (or clustering)
problem, our model optimizes for the separation regression error, ignoring the
order of mixing sources. This strategy cleverly solves the long-lasting label
permutation problem that has prevented progress on deep learning based
techniques for speech separation. Experiments on the equal-energy mixing setup
of a Danish corpus confirms the effectiveness of PIT. We believe improvements
built upon PIT can eventually solve the cocktail-party problem and enable
real-world adoption of, e.g., automatic meeting transcription and multi-party
human-computer interaction, where overlapping speech is common.Comment: 5 page
Segregating Event Streams and Noise with a Markov Renewal Process Model
DS and MP are supported by EPSRC Leadership Fellowship EP/G007144/1
Acoustic Scene Classification
This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship
(EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council
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