Speech synthesis using stochastic Markov graphs

Speech synthesis systems basing on concatenation of natural speech segments achieve a high quality in terms of naturalness and intelligibility. However, in many applications such systems are not easy to apply because of the huge demand for storage capacity. Speech synthesis systems based on HMMs could be an alternative to concatenative speech synthesis systems but do not yet achieve the quality needed for use in applications. In one of our research projects we investigate the possibility of combining speech synthesis and speech recognition to a unified system using the same databases and similar algorithms for synthesis and recognition. In this context we examine the suitability of stochastic Markov graphs instead of HMMs to improve the performance of such synthesis systems. The paper describes the training procedure we used to train the SMGs, explains the synthesis process and introduces an algorithm for state selection and state duration modeling. We focus particularly on issues which arise using SMGs instead of HMMs.