Abstract
A query-based hidden Markov model (HMM) training method for refinement of classification boundary is proposed. For this work, an HMM inversion with minimisation of mean-square error (MMSE) criterion is defined. The proposed algorithm is compared with conventional training methods on an isolated digit recognition problem. The proposed query-based HMM learning algorithm decreased the recognition error rate to 60% in conducted experiments.
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