Recent studies on speech separation show that the ideal binary mask (IBM) substantially improves speech intelligibility in noise. Supervised learning can be used to effectively estimate the IBM. However, supervised learning has trouble dealing with the situations where the probabilistic properties of the training data and the test data do not match, resulting in a challenging issue of generalization whereby the system trained under particular noise conditions may not generalize to new noise conditions. We propose to use a novel metric learning method to learn invariant speech features in the kernel space. As the learned features encode speech-related information that is robust to different noise types, the system is expected to generalize to unseen noise conditions. Evaluations show the advantage of the proposed approach over other speech separation systems.
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