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SVM Based Speaker Selection Using GMM Supervector for Rapid Speaker Adaptation

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Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

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Abstract

In this paper, we propose a novel method for rapid speaker adaptation called speaker support vector selection (SSVS). By taking gaussian mixture model (GMM) as speaker model, the speakers acoustically close to the test speaker are selected .Different from other selection method, just computing the likelihood between models, we utilizing support vector machines (SVM) to obtain a ‘more optimal speaker subset’. Such selection is dynamically determined according to the distribution of reference speakers close the test. Furthermore, a single-pass re-estimation procedure conditioned on the selected speakers is shown. This adaptation strategy was evaluated in a large vocabulary speech recognition task. The presented method improves the relative accuracy rates by 13% compared to the baseline system.

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Author information

Authors and Affiliations

  1. School of Information Engineering, Beijing University of Posts and Telecommunications, 100876, Beijing, China

    Jian Wang, Jianjun Lei, Jun Guo & Zhen Yang

Authors
  1. Jian Wang
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  2. Jianjun Lei
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  3. Jun Guo
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  4. Zhen Yang
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Editor information

Editors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung County Taiwan, ROC

    Tzai-Der Wang

  2. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  3. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  4. Department of Computer Science and Technology, University of Sci. & Tech. of China, 230026, Hefei, Anhui, P.R. China

    Xufa Wang

  5. School of Information Technology and Electrical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, Australia

    Hussein Abbass

  6. The Univertity of Tokyo, Japan

    Hitoshi Iba

  7. Department of Computer, University of Science and Technology of China, 230027, Hefei, China

    Guo-Liang Chen

  8. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, J., Lei, J., Guo, J., Yang, Z. (2006). SVM Based Speaker Selection Using GMM Supervector for Rapid Speaker Adaptation. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_78

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  • DOI: https://doi.org/10.1007/11903697_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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