A new HMM/LVQ hybrid algorithm for speech recognition
Shigeru Katagiri, C.-H. Lee · 2002
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%.>