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A variational EM algorithm for learning eigenvoice parameters in mixed signals

Ron J. Weiss; Daniel P. W. Ellis

Title:
A variational EM algorithm for learning eigenvoice parameters in mixed signals
Author(s):
Weiss, Ron J.
Ellis, Daniel P. W.
Date:
Type:
Articles
Department:
Electrical Engineering
Permanent URL:
Book/Journal Title:
2009 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings: April 19-24, 2009, Taipei International Convention Center, Taipei, Taiwan
Publisher:
IEEE
Abstract:
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.
Subject(s):
Electrical engineering
Applied mathematics
Item views:
38
Metadata:
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