Abstract：In this paper a optimization algorithm called constrained line search (CLS) is proposed for discriminative training (DT) of Gaussian mixture Continuous Density Hidden Markov Model (CDHMM) in speech recognition. The CLS method is used to optimize the objective function of Maximum Mutual Information (MMI) criterion based discriminative training. In this method, discriminative training of HMM is firstly treated as a constrained optimization problem, and Kullback-Leibler divergence (KLD) between models is explicitly applied as a constraint during optimization. Based upon the idea of line search, it is pointed out that a simple formula of HMM parameters can be expressed as a simple quadratic formula by constraining the KLD between HMM of two successive iterations. The proposed CLS method can be applied to optimize all model parameters in Gaussian mixture CDHMMs, including means, covariances, and mixture weights. The proposed CLS approach is investigated on two benchmark speech recognition tasks of both English and Chinese, including TIDIGITS and Mandarin 863 database. Experimental results show that the CLS optimization method consistently outperforms the conventional extended Baum-Welch (EBW) method in both recognition performance and convergence characteristic.
刘聪，胡郁，戴礼荣，王仁华. 一种针对区分性训练的受限线性搜索优化方法[J]. 模式识别与人工智能, 2010, 23(4): 450-455.
LIU Cong,HU Yu,DAI Li-Rong,WANG Ren-Hua. A Constrained Line Search Optimization Method for Discriminative Training of HMMs. , 2010, 23(4): 450-455.
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2010, Vol. 23 
