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Speaker Adaptation in a Large-Vocabulary Gaussian HMM Recognizer
September 1990 (vol. 12 no. 9)
pp. 917-920

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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Index Terms:
Gaussian HMM speech recognition; speaker adaptation; hidden Markov models; phoneme-dependent spectral mapping; heuristic; covariance matrices; Markov processes; matrix algebra; spectral analysis; speech recognition
Citation:
P. Kenny, M. Lennig, P. Mermelstein, "Speaker Adaptation in a Large-Vocabulary Gaussian HMM Recognizer," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 9, pp. 917-920, Sept. 1990, doi:10.1109/34.57686
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