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A Study on Regression Class Generation of MLLR Adaptation Using State Level Sharing  

오세진 (한국천문연구원 KVN 사업본부)
성우창 (영남대학교 전자정보공학부)
김광동 (한국천문연구원 KVN 사업본부)
노덕규 (한국천문연구원 KVN 사업본부)
송민규 (한국천문연구원 KVN 사업본부)
정현열 (영남대학교 전자정보공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.22, no.8, 2003 , pp. 727-739 More about this Journal
Abstract
In this paper, we propose a generation method of regression classes for adaptation in the HM-Net (Hidden Markov Network) system. The MLLR (Maximum Likelihood Linear Regression) adaptation approach is applied to the HM-Net speech recognition system for expressing the characteristics of speaker effectively and the use of HM-Net in various tasks. For the state level sharing, the context domain state splitting of PDT-SSS (Phonetic Decision Tree-based Successive State Splitting) algorithm, which has the contextual and time domain clustering, is adopted. In each state of contextual domain, the desired phoneme classes are determined by splitting the context information (classes) including target speaker's speech data. The number of adaptation parameters, such as means and variances, is autonomously controlled by contextual domain state splitting of PDT-SSS, depending on the context information and the amount of adaptation utterances from a new speaker. The experiments are performed to verify the effectiveness of the proposed method on the KLE (The center for Korean Language Engineering) 452 data and YNU (Yeungnam Dniv) 200 data. The experimental results show that the accuracies of phone, word, and sentence recognition system increased by 34∼37%, 9%, and 20%, respectively, Compared with performance according to the length of adaptation utterances, the performance are also significantly improved even in short adaptation utterances. Therefore, we can argue that the proposed regression class method is well applied to HM-Net speech recognition system employing MLLR speaker adaptation.
Keywords
State level sharing; PDT-SSS algorithm; Regression class; MLLR adaptation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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