Measuring Target Cost in Unit Selection with Kl-Divergence Between Context-Dependent HMMS

This paper proposes a new approach for measuring the target cost in unit selection, where the difference between the target and candidate units is estimated by the Kullback-Leibler divergence (KLD) between the context-dependent hidden Markov models (HMM). In order to model the left/right phonetic context, biphone models are generated by merging regular tri-phone HMMs sharing the same left/right phonetic context. To characterize prosodic contexts, various sets of prosody-sensitive monophone HMMs are trained. KLDs between these context models are calculated as the replacement cost between the contexts. Perceptual experiments show that the resulting synthesized speech sounds slightly better than those with the manually-tuned costs. An important advantage is that the proposed method can be conveniently applied to new corpora or languages without the need of collecting perceptual data