Abstract
This paper deals with a combination of basic adaptation techniques of Hidden Markov Model used in the speech recognition. The adaptation methods approach the data only through their statistics, which have to be accumulated before the adaptation process. When performing two adaptations subsequently, the data statistics have to be accumulated twice in each of the adaptation passes. However, when the adaptation methods are chosen with care, the data statistics may be accumulated only once, as proposed in this paper. This significantly reduces the time consumption and avoids the need to store all the adaptation data. Combination of Maximum A-Posteriori Probability and feature Maximum Likelihood Linear Regression adaptation is considered. Motivation for such an approach could be the on-line adaptation, where the time consumption is of big importance.
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References
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. In: Readings in speech recognition, pp. 267–296 (1990)
Psutka, J., Müller, L., Matoušek, J., Radová, V.: Mluvíme s počítačem česky, Academia, Praha (2007) ISBN:80-200-1309-1
Gauvain, L., Lee, C.H.: Maximum A-Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains. IEEE Transactions SAP 2, 291–298 (1994)
Alexander, A.: Forensic Automatic Speaker Recognition using Bayesian Interpretation and Statistical Compensation for Mismatched Conditions. Ph.D. thesis in Computer Science and Engineering, pp. 27-29, Indian Institute of Technology, Madras (2005)
Leggeter, C.J., Woodland, P.C.: Maximum Likelihood Linear Regression for Speaker Adaption of Continuous Density Hidden Markov Models. Computer Speech and Language 9, 171–185 (1995)
Gales, M.J.F.: Maximum Likelihood Linear Transformation for HMM-based Speech Recognition. Tech. Report, CUED/FINFENG/TR291, Cambridge Univ. (1997)
Povey, D., Saon, G.: Feature and Model Space Speaker Adaptation with Full Covariance Gaussians. In: Interspeech, paper 2050-Tue2BuP.14 (2006)
Gales, M.J.F.: The Generation and use of Regression class Trees for MLLR Adaptation, Cambridge University Engineering Department (1996)
Machlica, L., Zajíc, Z., Pražák, A.: Methods of Unsupervised Adaptation in Online Speech Recognition. In: Specom, St. Petersburg (2009)
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Zajíc, Z., Machlica, L., Müller, L. (2009). Refinement Approach for Adaptation Based on Combination of MAP and fMLLR. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2009. Lecture Notes in Computer Science(), vol 5729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04208-9_39
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DOI: https://doi.org/10.1007/978-3-642-04208-9_39
Publisher Name: Springer, Berlin, Heidelberg
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