Automatic speech segmentation based on acoustical clustering

Abstract

In this paper, we present an automatic speech segmentation system based on acoustical clustering plus dynamic time warping. Our system operates at three stages, the first one obtains a coarse segmentation as a starting point to the second one. The second stage fixes phoneme boundaries in an iterative process of progressive refinement. The third stage makes a finer adjustment by considering some acoustic parameters estimated at a higher subsampling rate around the boundary to be adjusted. No manually segmented utterances are used in any stage. The results presented here demonstrate a good learning capability of the system, which only uses the phonetic transcription of each utterance. Our approach obtains similar results than the ones reported by previous related works on TIMIT database. © 2010 Springer-Verlag Berlin Heidelberg.