Learning Hidden Markov Models Research Articles

Substitution matrices and hidden Markov models.

Hidden Markov models (HMMs) provide a general framework for expressing primary sequence consensus. HMMs can effectively be used to model and align protein families, and to search data bases. HMMs, however, have a large number of parameters. When only few sequences are available for model fitting, additional prior information must be incorporated into the models. We derive a simple algorithm that directly incorporates prior information provided by substitution matrices into the HMM learning procedure.

A new hybrid algorithm for speech recognition based on HMM segmentation and learning vector quantization

A hybrid speech recognition algorithm based on the combination of hidden Markov models (HMMs) and learning vector quantization (LVQ) is presented. The LVQ training algorithms are capable of producing highly discriminative reference vectors for classifying static patterns, i.e., vectors with a fixed dimension. The HMM formulation has also been successfully applied to the recognition of dynamic speech patterns that are of variable duration. It is shown that by combining both LVQ's discriminative power and the HMM's capability of modeling temporal variations of speech in a hybrid algorithm, the performance of the original HMM-based speech recognizer is significantly improved. For a highly confusable vocabulary consisting of the nine American English E-set letters used in a multispeaker, isolated-word test mode, the average word accuracy of the baseline HMM recognizer is 67%. When LVQ is incorporated in the hybrid system, the word accuracy increases to 83%. >