Abstract
Recently, Deep Neural Network (DNN), which is a feed-forward artificial neural network with many hidden layers, has opened a new research direction for Speech Synthesis. It can represent high dimension and correlated features efficiently and model highly complex mapping function compactly. However, the research on DNN-based Mongolian speech synthesis is still in blank filed. This paper applied the DNN-based acoustic model to Mongolian speech synthesis firstly, and built a Mongolian speech synthesis system according to the Mongolian character and acoustic features. Compared with the conventional HMM-based system under the same corpus, the DNN-based system can synthesize better Mongolian speech than HMM-based system can do. The Mean Opinion Score (MOS) of the synthesized Mongolian speech is 3.83. And it becomes a new state-of-the-art system in this field.
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Acknowledgments
This research was supports in part by the China national natural science foundation (No. 61563040, No. 61773224) and Inner Mongolian nature science foundation (No. 2016ZD06).
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Liu, R., Bao, F., Gao, G., Wang, Y. (2018). Mongolian Text-to-Speech System Based on Deep Neural Network. In: Tao, J., Zheng, T., Bao, C., Wang, D., Li, Y. (eds) Man-Machine Speech Communication. NCMMSC 2017. Communications in Computer and Information Science, vol 807. Springer, Singapore. https://doi.org/10.1007/978-981-10-8111-8_10
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