Factored MLLR Adaptation for HMM-Based Speech Synthesis in Naval-IT Fusion Technology 


Vol. 38,  No. 2, pp. 213-218, Feb.  2013


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  Abstract

One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In our previous study, we proposed factored MLLR (FMLLR) where each MLLR parameter is defined as a function of a control vector. We presented a method to train the FMLLR parameters based on a general framework of the expectation-maximization (EM) algorithm. Using the proposed algorithm, supplementary information which cannot be included in the models is effectively reflected in the adaptation process. In this paper, we apply the FMLLR algorithm to a pitch sequence as well as spectrum parameters. In a series of experiments on artificial generation of expressive speech, we evaluate the performance of the FMLLR technique and also compare with other approaches to parameter adaptation in HMM-based speech synthesis.

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  Cite this article

[IEEE Style]

J. S. Sung, D. H. Hong, M. A. Jeong, Y. Lee, S. R. Lee, N. S. Kim, "Factored MLLR Adaptation for HMM-Based Speech Synthesis in Naval-IT Fusion Technology," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 2, pp. 213-218, 2013. DOI: .

[ACM Style]

June Sig Sung, Doo Hwa Hong, Min A Jeong, Yeonwoo Lee, Seong Ro Lee, and Nam Soo Kim. 2013. Factored MLLR Adaptation for HMM-Based Speech Synthesis in Naval-IT Fusion Technology. The Journal of Korean Institute of Communications and Information Sciences, 38, 2, (2013), 213-218. DOI: .

[KICS Style]

June Sig Sung, Doo Hwa Hong, Min A Jeong, Yeonwoo Lee, Seong Ro Lee, Nam Soo Kim, "Factored MLLR Adaptation for HMM-Based Speech Synthesis in Naval-IT Fusion Technology," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 2, pp. 213-218, 2. 2013.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880