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Monaural Speech Separation using Source-Adapted Models

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2 Author(s)
Ron J. Weiss ; LabROSA, Dept. of Electrical Engineering, Columbia University. ; Daniel P. W. Ellis

We propose a model-based source separation system for use on single channel speech mixtures where the precise source characteristics are not known a priori. We do this by representing the space of source variation with a parametric signal model based on the eigenvoice technique for rapid speaker adaptation. We present an algorithm to infer the characteristics of the sources present in a mixture, allowing for significantly improved separation performance over that obtained using unadapted source models. The algorithm is evaluated on the task defined in the 2006 Speech Separation Challenge [1] and compared with separation using source-dependent models.

Published in:

2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

Date of Conference:

21-24 Oct. 2007