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Deep Neural Network (DNN), which can model a long-span, intricate transform compactly with a deep-layered structure, has recently been investigated for parametric TTS synthesis with a fairly large corpus (33,000 utterances) [6]. In this paper, we examine DNN TTS synthesis with a moderate size corpus of 5 hours, which is more commonly used for parametric TTS training. DNN is used to map input text...
This paper presents a Rich-context Unit Selection (RUS) approach to high quality speech synthesis. Based upon our previous work on rich context modeling, we use the corresponding parametric HMMs to represent waveform units and form a “sausage-like” lattice. A prune-and-search procedure is proposed, in which Kullback-Leibler divergence is adopted to select potential candidate units, and normalized...
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