Exploration of vowel onset and offset points for hybrid speech segmentation

Automatic segmentation of speech using embedded reestimation of monophone hidden Markov models (HMMs) followed by forced alignment may not give accurate boundaries. Group delay (GD) processing for refining the boundaries at the syllable level is attempted earlier. This paper aims at exploring vowel onset point (VOP) and vowel offset or end point (VEP) for correcting the boundaries obtained using HMM alignment. HMM models the class information well, however may not detect the exact boundary. In case of VOPs and VEPs, spurious rate or miss rate can be there, but detected boundaries are more accurate. Combining both HMM and VOP/VEP gives improvement in terms of log likelihood scores of forced aligned phoneme boundaries. HMM boundaries are corrected using VOP/VEP and model parameters are reestimated at the syllable level. Results are compared with that of GD based correction and found that overall performance is comparable. Performance for vowels is found to be higher than that of GD based refinement as the refinement in this case is mainly at the vowel boundaries. HMM based speech synthesis systems (HTS) are developed using phone as a basic unit with the proposed segmentation method. Subjective evaluation indicates that there is an improvement in the quality of synthesis.