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http://dx.doi.org/10.7840/kics.2013.38C.2.213

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

Sung, June Sig (서울대학교 전기컴퓨터공학부 뉴미디어통신공동연구소)
Hong, Doo Hwa (서울대학교 전기컴퓨터공학부 뉴미디어통신공동연구소)
Jeong, Min A (목포대학교)
Lee, Yeonwoo (목포대학교)
Lee, Seong Ro (목포대학교)
Kim, Nam Soo (서울대학교 전기컴퓨터공학부)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.38C, no.2, 2013 , pp. 213-218 More about this Journal
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.
Keywords
Speech synthesis; adaptation; MLLR; HMM; expressive speech;
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