Eigenvoice modeling with sparse training data

We derive an exact solution to the problem of maximum likelihood estimation of the supervector covariance matrix used in extended MAP (or EMAP) speaker adaptation and show how it can be regarded as a new method of eigenvoice estimation. Unlike other approaches to the problem of estimating eigenvoice...

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Bibliographic Details
Published in:IEEE transactions on speech and audio processing 2005-05, Vol.13 (3), p.345-354
Main Authors: Kenny, P., Boulianne, G., Dumouchel, P.
Format: Article
Language:English
Subjects:
Applied sciences
Cluster adaptive training
Clusters
Covariance matrix
Eigenvalues and eigenfunctions
eigenvoices
Equivalence
Exact sciences and technology
Exact solutions
extended MAP (EMAP)
H infinity control
Hidden Markov models
Infinity
Information, signal and communications theory
Loudspeakers
Mathematical analysis
Mathematical models
Maximum likelihood estimation
Principal component analysis
Signal processing
speaker adaptation
Speech
Speech processing
Speech recognition
Telecommunications and information theory
Testing
Training
Training data
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Summary:We derive an exact solution to the problem of maximum likelihood estimation of the supervector covariance matrix used in extended MAP (or EMAP) speaker adaptation and show how it can be regarded as a new method of eigenvoice estimation. Unlike other approaches to the problem of estimating eigenvoices in situations where speaker-dependent training is not feasible, our method enables us to estimate as many eigenvoices from a given training set as there are training speakers. In the limit as the amount of training data for each speaker tends to infinity, it is equivalent to cluster adaptive training.
ISSN:1063-6676
2329-9290
1558-2353
2329-9304
DOI:10.1109/TSA.2004.840940