A minimum-distortion segmentation/LVQ hybrid algorithm for speech recognition

The quick and simple training of learning vector quantization (LVQ) can produce classi ficationpower at least as high as that by a powerful, but complex classifier based on artificial neural networks. However, LVQ is a discriminative training algorithm for a distance classifier handling static (fixed-dimensional) patterns. Thus, an innovative process is required to apply this algorithm to dynamic (variable-durational) speech patterns. To meet this requirement, an HHM/LVQ hybrid algorithm was proposed which integrated HMM (Viterbi) segmentation with LVQ classification. However, this algorithm, using all the possible HMM models for segmentation, produces an enormous number of training tokens, making it difficult to apply to large-scale continuous speech recognition tasks. In this light, we present a new minimum-distortion segmentation (MDS)/discriminative classification hybrid algorithm. The MDS algorithm produces one segmentation and this is used in place of the many HMM segmentations. To make a proper comparison between the two methods we used as our discriminative classifier the same LVQ formulation. For clarity, we refer to this proposed algorithm as an MDS/LVQ hybrid algorithm. Results on the E-set task show that MDS/LVQ, with its significantly reduced training, can achieve discriminative power at least as high as HMM/LVQ.