Abstract
Deep neural networks (DNNs) have been recently introduced in speech synthesis. In this paper, an investigation on the importance of input features and training data on speaker dependent (SD) DNN-based speech synthesis is presented. Various aspects of the training procedure of DNNs are investigated in this work. Additionally, several training sets of different size (i.e., 13.5, 3.6 and 1.5 h of speech) are evaluated.
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Lazaridis, A., Potard, B., & Garner, P. N. (2015). DNN-based speech synthesis: Importance of input features and training data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9319, pp. 193–200). Springer Verlag. https://doi.org/10.1007/978-3-319-23132-7_24
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