On-line adaptation of the SCHMM parameters based on the segmental quasi-Bayes learning for speech recognition
نویسندگان
چکیده
In this correspondence, on-line quasi-Bayes adaptation of the mixture coefficients and mean vectors in semicontinuous hidden Markov model (SCHMM) is studied. The viability of the proposed algorithm is confirmed and the related practical issues are addressed in a specific application of on-line speaker adaptation using a 26-word English alphabet vocabulary.
منابع مشابه
Title On-line adaptation of the SCHMM parameters based on the segmental quasi-bayes learning for speech recognition
In this correspondence, on-line quasi-Bayes adaptation of the mixture coefficients and mean vectors in semicontinuous hidden Markov model (SCHMM) is studied. The viability of the proposed algorithm is confirmed and the related practical issues are addressed in a specific application of on-line speaker adaptation using a 26-word English alphabet vocabulary.
متن کاملHidden Markov Model for Speech Recognition
In this paper, a theoretical framework for Bayesian adaptive training of the parameters of discrete hidden Markov model (DHMM) and of semi-continuous HMM (SCHMM) with Gaussian mixture state observation densities is presented. In addition to formulating the forward-backward MAP (maximum a posterion’) and the segmental MAP algorithms for estimating the above HMM parameters, a computationally effi...
متن کاملSpeaker Adaptation in Continuous Speech Recognition Using MLLR-Based MAP Estimation
A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...
متن کاملSpeaker Adaptation in Continuous Speech Recognition Using MLLR-Based MAP Estimation
A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...
متن کاملBayesian adaptive learning of the parameters of hidden Markov model for speech recognition
In this paper a theoretical framework for Bayesian adaptive learning of discrete HMM and semi continuous one with Gaussian mixture state observation densities is presented Corre sponding to the well known Baum Welch and segmental k means algorithms respectively for HMM training formulations of MAP maximum a posteriori and segmental MAP estima tion of HMM parameters are developed Furthermore a c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- IEEE Trans. Speech and Audio Processing
دوره 4 شماره
صفحات -
تاریخ انتشار 1996