Kernel based Clustering and Vector Quantization for Speech Segmentation

In this paper, we propose an approach to segmentation of continuous speech into syllable-like units where each unit has one or more consonants followed by a vowel. The proposed approach uses the clustering and vector quantization methods to identify the consonant, transition and vowel regions in continuous speech. We consider methods based on clustering and vector quantization in the Mercer kernel feature space for separation of nonlinearly separable clusters of data belonging to the different regions. Results of experimental studies demonstrate the effectiveness of the kernel based methods in improving the performance of the speech segmentation system.