Abstract
This paper proposes a new phoneme recognition method based on the Learning Vector Quantization (LVQ2) algorithm proposed by Kohonen. We propose three versions of a modified training algorithm to overcome a shortcoming of the LVQ2 method. In the modified LVQ2 algorithm, p reference vectors are modified at the same time if the correct class is within the N-th rank where N is set to some constant. Using this al-gorithm, the phoneme recognition scores obtained by the modified LVQ2 algorithm were higher than those obtained by the original LVQ2 algorithm. Furthermore, we propose a segmentation and recognition method for phonemes in continuous speech. At first a likelihood matrix is computed using the reference vectors, where each row indicates the likelihood sequence of each phoneme and each column indicates the likeli-hood of all phonemes for each 10-ms unit. The optimum phoneme sequence is com-puted from the likelihood matrix using the DP with duration constraints. We 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 speech samples in continuous speech.
