Mongolian Text-to-Speech System Based on Deep Neural Network

  • Rui Liu
  • Feilong BaoEmail author
  • Guanglai Gao
  • Yonghe Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 807)


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.


Mongolian Text-to-Speech (TTS) Acoustic model Deep Neural Network (DNN) 



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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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Rui Liu
    • 1
  • Feilong Bao
    • 1
    Email author
  • Guanglai Gao
    • 1
  • Yonghe Wang
    • 1
  1. 1.College of Computer ScienceInner Mongolia UniversityHohhotChina

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