On-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition

作     者：Huo, Q Lee, CH 

作者机构：ATR Interpreting Telecommun Res Labs Kyoto 61902 Japan 

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

年 卷 期：1998年第6卷第4期

页      面：386-397页

核心收录：

主　　题：automatic speech recognition continuous density hidden Markov models EM algorithm recursive Bayesian estimation speaker adaptation 

摘      要：We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models (HMM s) with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors updating. As an example, by applying the method to on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotically convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different model parameters. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments, and so on.