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Text-independent Speaker Recognition Using Radial Basis Function Network

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Advances in Neural Networks – ISNN 2016 (ISNN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9719))

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Abstract

Radial Basis Function Neural Network (RBFNN) is proposed as a solution to the text-independent speaker recognition problem. Recognition is based on estimation of sufficiently large set of acoustic features, construction of multidimensional histograms and approximation arbitrary distributions of the components of acoustic features with probability density functions (PDF), with possibility of wide shape variation. Proposed method allowed to reduce the probability of errors in the decision making.

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References

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Author information

Authors and Affiliations

  1. Institute of Computer Science and Technology, Peter the Great Saint-Petersburg Polytechnic University, Polytechnicheskaya 21, 194021, St. Petersburg, Russia

    Anton Yakovenko & Galina Malychina

Authors
  1. Anton Yakovenko
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  2. Galina Malychina
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Corresponding author

Correspondence to Galina Malychina .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. Huazhong University of Science and Tech., Wuhan, Jiangsu, China

    Qingshan Liu

  3. Russian Academy of Sciences, SPIIRAS, St. Petersburg, Russia

    Andrey Ronzhin

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© 2016 Springer International Publishing Switzerland

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Yakovenko, A., Malychina, G. (2016). Text-independent Speaker Recognition Using Radial Basis Function Network. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-40663-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40662-6

  • Online ISBN: 978-3-319-40663-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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