【摘要】 将混合因子分析方法与隐马尔可夫模型技术相结合,构造了一种新的统计声学模型━基于混合因子分析的隐马尔可夫模型(Hidden Markov Model based on Mixture of Factor Analysis:HMM-MFA)。重点研究了HMM-MFA的训练算法。通过推广著名的Baum辅助函数,并用拉格朗日多乘子方法,导出了HMM-MFA的参数重估公式。仿真结果表明,提出的算法在识别精度上优于传统的EM算法。更多还原

【Abstract】 Combining mixture of factor analysis method with hidden Markov modeling techniques,a new statistical acoustic model was constructed:hidden Markov model based on mixture of factor analysis(HMM-MFA).The HMM-MFA models the correlation between the feature vector elements in speech signals.The training algorithm for HMM-MFA was studied,by generalizing Baum’s auxiliary function into this framework and an associated objective function was built up.The training equations for estimating parameters of HMM-MFA was derived by Lagrange multiplier method.Simulation shows that the proposed algorithm is better than the traditional EM algorithm in speech recognition accuracy.更多还原