Statistical Methods for Speech Transmission Using Hidden Markov Models
نویسندگان
چکیده
This work considers the problem of Bayesian estimation of a hidden Markov source corrupted by additive noise. We develop sequential and complete sequence Bayesian de-coders for noisy sources with memory and apply them to the log-area ratio (LAR) coeecients of speech corrupted by additive white Gaussian noise. To this end, we follow a model-based approach in which the source is approximated by a hidden Markov model. A suboptimal Bayesian estimator whose performance closely approximates the optimal Bayesian estimator is also derived.
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