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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Acknowledgements
This work has received funding from the Swiss National Science Foundation under the SIWIS project and was supported by Eurostars Programme powered by Eurostars and the European Community under the project “D-Box: A generic dialog box for multi-lingual conversational applications”.
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Lazaridis, A., Potard, B., Garner, P.N. (2015). DNN-Based Speech Synthesis: Importance of Input Features and Training Data. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds) Speech and Computer. SPECOM 2015. Lecture Notes in Computer Science(), vol 9319. Springer, Cham. https://doi.org/10.1007/978-3-319-23132-7_24
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DOI: https://doi.org/10.1007/978-3-319-23132-7_24
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