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ICASSP
2009
IEEE

A variational EM algorithm for learning eigenvoice parameters in mixed signals

10 years 5 months ago
A variational EM algorithm for learning eigenvoice parameters in mixed signals
We derive an efficient learning algorithm for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a priori. The sources are modeled using factor-analyzed hidden Markov models (HMM) where source specific characteristics are captured by an “eigenvoice” speaker subspace model. The proposed algorithm is able to learn adaptation parameters for two speech sources when only a mixture of signals is observed. We evaluate the algorithm on the 2006 Speech Separation Challenge data set and show that it is significantly faster than our earlier system at a small cost in terms of performance.
Ron J. Weiss, Daniel P. W. Ellis
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Ron J. Weiss, Daniel P. W. Ellis
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