An algorithm for maximum likelihood estimation of hidden Markov models with unknown state-tying

作     者：Cappe, O Mokbel, CE Jouvet, D Moulines, E 

作者机构：ENST Dept Signal CNRS URA 820 F-75634 Paris 13 France 

出 版 物：《IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING》 (IEEE Trans Speech Audio Process)

年 卷 期：1998年第6卷第1期

页      面：61-70页

核心收录：

主　　题：expectation-maximization algorithm hidden Markov models maximum likelihood estimation speech recognition 

摘      要：For speech recognition based on hidden Markov modeling, parameter-tying, which consists in constraining some of the parameters of the model to share the same value, has emerged as a standard practice. In this paper, an original algorithm is proposed that makes it possible to jointly estimate both the shared model parameters and the tying characteristics;using the maximum likelihood criterion, The proposed algorithm is based on a recently introduced extension of the classic expectation-maximization (EM) framework. The convergence properties of this class of algorithms are analyzed in detail. The method is evaluated on an isolated word recognition task using hidden Markov models (HMM s) with Gaussian observation densities and tying at the state level. Finally, the extension of this method to the case of mixture observation densities with tying at the mixture component level is discussed.