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Speaker Verification Method Based on Two-Layer GMM-UBM Model in the Complex Environment

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Brain Informatics (BI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10654))

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Abstract

In order to improve speaker verification accuracy in the complex environment, a two-layer Gaussian mixture model-universal background model (GMM-UBM) model based on speaker verification method is proposed. For different layer, a GMM-UBM model was trained by different combination of speech features. The voice data of 3 days (36 h) were recorded from the complex environment, and the collected data was manually segmented into four classes: quiet, noise, target speaker and other speaker. Not only the segment data can be used to train GMM-UBM model, but also it can provide a criterion to assess the effectiveness of the model. The results show that the highest recall for the second and third day were 0.75 and 0.74 respectively, and the corresponding specificity were 0.29 and 0.19, which indicates the proposed GMM-UBM model is viable to verify the target speaker in the complex environment.

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Acknowledgments

This work is partially supported by the National Basic Research Program of China (No. 2014CB744600), National Natural Science Foundation of China (No. 61420106005), Beijing Natural Science Foundation (No. 4164080), and Beijing Outstanding Talent Training Foundation (No. 2014000020124G039).

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Authors and Affiliations

  1. International WIC Institute, Beijing University of Technology, Beijing, 100024, China

    Qiang He, Haiyan Zhou & Ning Zhong

  2. Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China

    Qiang He, Haiyan Zhou & Ning Zhong

  3. Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China

    Qiang He, Haiyan Zhou & Ning Zhong

  4. Beijing Anding Hospital of Capital Medical University, Beijing, China

    Jie Yang

  5. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi-City, 371-0816, Japan

    Zhijiang Wan & Ning Zhong

Authors
  1. Qiang He
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  2. Zhijiang Wan
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  3. Haiyan Zhou
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  4. Jie Yang
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  5. Ning Zhong
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Corresponding author

Correspondence to Ning Zhong .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  2. Beijing Normal University, Beijing, China

    Yong He

  3. KTH Royal Institute of Technology and Karolinska Institute, Stockholm, Sweden

    Jeanette Hellgren Kotaleski

  4. University of California, San Diego, San Diego, California, USA

    Maryann Martone

  5. Chinese Academy of Sciences, Beijing, China

    Bo Xu

  6. Allen Institute for Brain Science, Seattle, Washington, USA

    Hanchuan Peng

  7. Wuhan National Lab Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Qingming Luo

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He, Q., Wan, Z., Zhou, H., Yang, J., Zhong, N. (2017). Speaker Verification Method Based on Two-Layer GMM-UBM Model in the Complex Environment. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-70772-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70771-6

  • Online ISBN: 978-3-319-70772-3

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