Robust splicing costs and efficient search with BMM Models for concatenative speech synthesis
With the growing popularity of corpus-based methods for concatenative speech synthesis, a large amount of interest has been placed on borrowing techniques from the ASR community. This paper explores the applications of Buried Markov Models (BMM) to speech synthesis. We show that BMMs are more efficient than HMMs as a synthesis model, and focus on using BMM dependencies for computing splicing costs. We also show how the computational complexity of the dynamic search can be significantly reduced by constraining the splicing points with a negligible loss in synthesis quality.