Speaker adaptation in a large-vocabulary Gaussian HMM recognizer
Kenny, P.
Lennig, M.
Mermelstein, P.
INRS-Telecommun., Montreal, Que.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Sep 1990
Volume: 12,
Issue: 9
On page(s): 917-920
ISSN: 0162-8828
References Cited: 12
CODEN: ITPIDJ
INSPEC Accession Number: 3758656
Digital Object Identifier: 10.1109/34.57686
Current Version Published: 2002-08-06
Abstract
The problem of using a small amount of speech data to adapt a set
of Gaussian HMMs (hidden Markov models) that have been trained on one
speaker to recognize the speech of another is considered. The authors
experimented with a phoneme-dependent spectral mapping for adapting the
mean vectors of the multivariate Gaussian distributions (a method
analogous to the confusion matrix method that has been used to adapt
discrete HMMs), and a heuristic for estimating covariance matrices from
small amounts of data. The best results were obtained by training the
mean vectors individually from the adaptation data and using the
heuristic to estimate distinct covariance matrices for each
phoneme
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