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Non-negative Matrix Factorization (NMF) has already been applied to learnspeaker characterizations from single or non-simultaneous speech for speakerrecognition applications. It is also known for its good performance in (blind)source separation for simultaneous speech. This paper explains how NMF can beused to jointly solve the two problems in a multichannel speaker recognizer forsimultaneous speech. It is shown how state-of-the-art multichannel NMF forblind source separation can be easily extended to incorporate speakerrecognition. Experiments on the CHiME corpus show that this method outperformsthe sequential approach of first applying source separation, followed byspeaker recognition that uses state-of-the-art i-vector techniques.

Joint Sound Source Separation and Speaker Recognition