Abstract：The hidden Markov model based on diagonal covariance matrix Gaussian distributions (HMM-DG) is at present the most popular and successful model in speech recognition.However,there are well-known shortcomings in HMM-DG particularly in the modeling of the correlation among feature-vector elements.This paper investigates the combined use of mixture Gaussian models and factor analysis in HMM.We propose a hidden Markov model based on factor analysis (HMM-FA) and derive an expectation-maximization(EM) algorithm for maximum likelihood estimation.Our theoretical analysis and computer simulation show that the HMM-FA can achieve better performances over HMM-DG with the same amount of training data.
收稿日期: 2004-02-25
引用本文:
王新民. 基于因子分析的隐马尔可夫模型[J]. , 2004, 43(2): 0-0.
王新民. A hidden Markov model based on factor analysis. , 2004, 43(2): 0-0.
