论文信息 - Maximum mutual information estimation of hidden Markov model parameters for speech recognition

Maximum mutual information estimation of hidden Markov model parameters for speech recognition

A method for estimating the parameters of hidden Markov models of speech is described. Parameter values are chosen to maximize the mutual information between an acoustic observation sequence and the corresponding word sequence. Recognition results are presented comparing this method with maximum likelihood estimation.

[1] L. Baum,et al. An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[2] Lalit R. Bahl,et al. A Maximum Likelihood Approach to Continuous Speech Recognition , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3] Michael Picheny,et al. Some experiments with large-vocabulary isolated-word sentence recognition , 1984, ICASSP.