An introduction of trajectory model into HMM-based speech synthesis

In the synthesis part of a hidden Markov model (HMM) based speech synthesis system which we have proposed, a speech parameter vector sequence is generated from a sentence HMM corresponding to an arbitrarily given text by using a speech parameter generation algorithm. However, there is an inconsistency: although the speech parameter vector sequence is generated under the constraints between static and dynamic features, HMM parameters are trained without any constraints between them in the same way as standard HMM training. In the present paper, we introduce a trajectory-HMM, which has been derived from the HMM under the constraints between static and dynamic features, into the training part of the HMM-based speech synthesis system. Experimental results show that the use of trajectory-HMM training improves the quality of the synthesized speech.