Subspace and hypothesis based effective segmentation of co-articulated basic-units for concatenative speech synthesis

In this paper, we present two new methods for vowel-consonant segmentation of a co-articulated basic-units employed in our Thirukkural Tamil text-to-speech synthesis system (G. L. Jayavardhana Rama et al, IEEE workshop on Speech Synthesis, 2002). The basic-units considered in this are CV, VC, VCV, VCCV and VCCC, where C stands for a consonant and V for any vowel. In the first method, we use a subspace-based approach for vowel-consonant segmentation. It uses oriented principal component analysis (OPCA) where the test feature vectors are projected on to the V and C subspaces. The crossover of the norm-contours obtained by projecting the test basic-unit onto the V and C subspaces give the segmentation points which in turn helps in identifying the V and C durations of a test basic-unit. In the second method, we use probabilistic principal component analysis (PPCA) to get probability models for V and C. We then use the Neymen-Pearson (NP) test to segment the basic-unit into V and C. Finally, we show that the hypothesis testing turns out to be an energy detector for V-C segmentation which is similar to the first method.