DNN-based stochastic postfilter for HMM-based speech synthesis
Ling-Hui Chen, Tuomo Raitio, Cassia Valentini-Botinhao, Junichi Yamagishi, Zhen-Hua Ling · 2014
In this paper we propose a deep neural network to model the conditional probability of the spectral differences between nat-ural and synthetic speech. This allows us to reconstruct the spectral fine structures in speech generated by HMMs. We com-pared the new stochastic data-driven postfilter with global vari-ance based parameter generation and modulation spectrum en-hancement. Our results confirm that the proposed method sig-nificantly improves the segmental quality of synthetic speech compared to the conventional methods. Index Terms: HMM, speech synthesis, DNN, modulation spectrum, postfilter, segmental quality