Continuous speech recognition with modified learning vector quantization algorithm and two-level DP-matching

The authors propose a phoneme recognition method based on the learning vector quantization (LVQ2) algorithm. They propose three kinds of modified training algorithms for the LVQ2 algorithm. In the recognition stage, the likelihood matrix is computed using the reference vectors and then the optimum phoneme sequence is computed from the matrix using the two-level dynamic programming (DP)-matching with duration constraints. The recognition score of phonemes in isolated spoken words was 89.1% for the test set. The phoneme recognition scores obtained by the modified LVQ2 algorithm were higher than those obtained by the original LVQ2 algorithm. The authors applied this method to a multi-speaker-dependent phoneme recognition task for continuous speech uttered Bunsetsu-by-Bunsetsu. The phoneme recognition score was 85.5% for the test speech samples in continuous speech