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Fast ICA for Multi-speaker Recognition System

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Advanced Intelligent Computing Theories and Applications (ICIC 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 93))

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Abstract

It is hard to recognize speakers when the samples of voice are mixed. To overcome this shortcoming, this paper proposes a method: firstly, fast independent component analysis (Fast ICA) method is used for separating mixed voice signal of speakers. Secondly, a model of RBF neural network is used for speaker recognition. Based on the idea of blind signal separation, Fast ICA method can be used for signal separation because different voice signal source maintain a relatively independent identity. In this research of Multi-speaker recognition, the features can be extracted from the separated speech signals and a RBF neural network is used for the recognition model. Experiment results show that, this is an effective method for the mixed-voice speaker recognition.

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Author information

Authors and Affiliations

  1. Department of electronics & information engineering, Suzhou Vocational University, Suzhou, China, 215104

    Yan Zhou & Zhiqiang Zhao

Authors
  1. Yan Zhou
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  2. Zhiqiang Zhao
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui,, China

    De-Shuang Huang

  2. School of Computing and Intelligent Systems, University of Ulster at Magee Campus, BT48 7JL, Derry, Northern Ireland, UK

    Martin McGinnity

  3. Laboratoire LITIS, Université de Rouen, 76800, Saint Etienne du Rouvray, France

    Laurent Heutte

  4. Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada

    Xiao-Ping Zhang

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© 2010 Springer-Verlag Berlin Heidelberg

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Zhou, Y., Zhao, Z. (2010). Fast ICA for Multi-speaker Recognition System. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_66

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  • DOI: https://doi.org/10.1007/978-3-642-14831-6_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14830-9

  • Online ISBN: 978-3-642-14831-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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