DECISION-TREE BACKING-OFF IN HMM-BASED SPEECH SYNTHESIS

摘要

This paper proposes a decision-tree backing-off technique for an HMM-based speech synthesis system. In the system, a decision-tree based context clustering technique is used for constructing parameter tying structures. In the context clustering, the MDL criterion has been used as a stopping criterion. In this paper, however, huge decision-trees are constructed without any stopping criterion. In the synthesis phase, decision-trees obtained in this way are used in the proposed backing-off scheme. This enables us to adjust the cluster size dynamically at runtime according to the text to be synthesized. Results of subjective listening tests show that the proposed technique improves the synthesized speech quality.