Summary
We investigate the Bayesian Learning approach (also known as Maximum A Posteriori — MAP) to the speaker adaptation of Continuous Density Hidden Markov Models (CDHMMs). The parameters of the Gaussian mixture output densities are adapted using the exponential forgetting mechanism and performing the a priori parameter estimation in a model based outline. Moreover a channel adaptation is carried out by means of the cepstral mean normalization method (CMN).
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References
L. Fissore, F. Ravera, and P. Laface. Acoustic-phonetic modeling for flexible vocabulary speech recognition. In Proc. of EUROSPEECH, pages 1–799-802. Madrid, Spain, 1995.
J.-L. Gauvain and C.-H. Lee. Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains. IEEE Trans. on Speech and Audio Processing, 2 (2): 291–298, Apr. 1992.
Q. Huo and C.-H. Lee. A study of on-line quasi-bayes adaptation for cdhmm-based speech recognition. In Proc. of lCASSP, pages II–705–708. Atlanta, 1996.
Y. Zhao. Self-learning speaker and channel adaptation based on spectral variation source decomposition. Speech Communication, 18: 65–77, Jan. 1996.
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© 1999 Springer-Verlag Berlin Heidelberg
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Vair, C., Fissore, L. (1999). Speaker Adaptation of CDHMMs Using Bayesian Learning. In: Ponting, K. (eds) Computational Models of Speech Pattern Processing. NATO ASI Series, vol 169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60087-6_7
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DOI: https://doi.org/10.1007/978-3-642-60087-6_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-64250-0
Online ISBN: 978-3-642-60087-6
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