Statistical parametric speech synthesis based on product of experts

Multiple-level acoustic models (AMs) are often combined in statistical parametric speech synthesis. Both linear and non-linear functions of the observation sequence are used as features in these AMs. This combination of multiple-level AMs can be expressed as a product of experts (PoE); the likelihoods from the AMs are scaled, multiplied together and then normalized. Currently these multiple-level AMs are individually trained and only combined at the synthesis stage. This paper discusses a more consistent PoE framework where the AMs are jointly trained. A generalization of trajectory HMM training can be used for multiple-level Gaussian AMs based on linear functions. However for the non-linear case this is not possible, so a scheme based on contrastive divergence learning is described. Experimental results show that the proposed technique provides both a mathematically elegant way to train multiple-level AMs and statistically significant improvements in the quality of synthesized speech.