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It is shown that by combining the discriminative power of learning vector quantization (LVQ) training algorithms and the capability of modeling temporal variations of a hidden Markov model (HMM) into a hybrid algorithm, the performance of an HMM-based recognition algorithm is significantly improved. The hybrid algorithm was tested in a multispeaker, isolated word mode, using a highly confusable vocabulary consisting of the nine English E-set words. The average word accuracy for the original HMM-based system was 62%. When the LVQ classifier was incorporated, the word accuracy increased to 81%.>
Katagiri et al. (Wed,) studied this question.
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