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Mongolian Speech Recognition Based on Deep Neural Networks

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (CCL 2015, NLP-NABD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9427))

Abstract

Mongolian is an influential language. And better Mongolian Large Vocabulary Continuous Speech Recognition (LVCSR) systems are required. Recently, the research of speech recognition has achieved a big improvement by introducing the Deep Neural Networks (DNNs). In this study, a DNN-based Mongolian LVCSR system is built. Experimental results show that the DNN-based models outperform the conventional models which based on Gaussian Mixture Models (GMMs) for the Mongolian speech recognition, by a large margin. Compared with the best GMM-based model, the DNN-based one obtains a relative improvement over 50 %. And it becomes a new state-of-the-art system in this field.

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Acknowledgements

This research was supported in part by the China national nature science foundation (No.61263037), Inner Mongolia nature science foundation (No. 2014BS0604) and the program of high-level talents of Inner Mongolia University.

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Authors and Affiliations

  1. College of Computer Science, Inner Mongolia University, Hohhot, 010021, China

    Hui Zhang, Feilong Bao & Guanglai Gao

Authors
  1. Hui Zhang
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  2. Feilong Bao
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  3. Guanglai Gao
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Corresponding author

Correspondence to Feilong Bao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Tsinghua University, Beijing, China

    Zhiyuan Liu

  3. Soochow University, Suzhou, Jiangsu, China

    Min Zhang

  4. Tsinghua University, Beijing, China

    Yang Liu

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Zhang, H., Bao, F., Gao, G. (2015). Mongolian Speech Recognition Based on Deep Neural Networks. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_15

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  • DOI: https://doi.org/10.1007/978-3-319-25816-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25815-7

  • Online ISBN: 978-3-319-25816-4

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