마진이 강화된 최대 상호 정보 방법을 이용한 은닉 마르코프 모델의 매개변수 추정Margin-Enhanced Maximum Mutual Information Estimation for Hidden Markov Models

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We considered a discriminative training algorithm to estimate continuous-density hidden Markov model for speech recognition. The proposed algorithm, called margin-enhanced maximum mutual information (MEMMI), is to maximize the weighted sum of the maximum mutual information objective function and the large margin objective function. The MEMMI leads to a simple objective function that can be optimized easily by a gradient ascent algorithm maintaining a probabilistic model. Experimental results show that the recognition accuracy of the MEMMI is better than other discriminative training criteria on the TIDIGITS database.
Publisher
대한전자공학회
Issue Date
2009-07
Language
KOR
Citation

대한전자공학회 하계종합학술대회 , pp.983 - 984

URI
http://hdl.handle.net/10203/162862
Appears in Collection
EE-Conference Papers(학술회의논문)
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