Improved prosody generation by maximizing joint likelihood of state and longer units

The current state-of-art HMM-bsed TTS can produce highly intelligible output speech and deliver a decent segmental quality. However, its prosody, especially at the phrase or sentence level, tends to be bland. The blandness of synthesized prosody is partially due to the fact that a state-based HMM is rather inadequate in modeling a global, hierarchical prosodic structure at a sentence or phrase level. In this study, the prosody of longer units are first modeled explicitly by appropriate parametric distributions. The resultant models are then integrated with the state-level baseline models to generate an optimal prosody by maximizing the joint likelihood of all, from state to longer, units. Experimental results in both Mandarin and English show consistent improvements over the state-based baseline system. The improvements are both objectively measurable and subjectively perceivable.