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International Conference on Contemporary Computing

IC3 2009: Contemporary Computing pp 307–317Cite as

Text and Language-Independent Speaker Recognition Using Suprasegmental Features and Support Vector Machines

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 40))

Abstract

In this paper, presence of the speaker-specific suprasegmental information in the Linear Prediction (LP) residual signal is demonstrated. The LP residual signal is obtained after removing the predictable part of the speech signal. This information, if added to existing speaker recognition systems based on segmental and subsegmental features, can result in better performing combined system. The speaker-specific suprasegmental information can not only be perceived by listening to the residual, but can also be seen in the form of excitation peaks in the residual waveform. However, the challenge lies in capturing this information from the residual signal. Higher order correlations among samples of the residual are not known to be captured using standard signal processing and statistical techniques. The Hilbert envelope of residual is shown to further enhance the excitation peaks present in the residual signal. A speaker-specific pattern is also observed in the autocorrelation sequence of the Hilbert envelope, and further in the statistics of this autocorrelation sequence. This indicates the presence of the speaker-specific suprasegmental information in the residual signal. In this work, no distinction between voiced and unvoiced sounds is done for extracting these features. Support Vector Machine (SVM) is used to classify the patterns in the variance of the autocorrelation sequence for the speaker recognition task.

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Author information

Authors and Affiliations

  1. DeciDyn Systems, Bangalore, India

    Anvita Bajpai

  2. Dept. of Computer Science and Engineering, Indian Institute of Technology Madras, India

    Vinod Pathangay

Authors
  1. Anvita Bajpai
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  2. Vinod Pathangay
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Editor information

Editors and Affiliations

  1. Dept. of Computer Sciences, University of Florida, Gainesville, FL, 32611, USA

    Sanjay Ranka

  2. Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, USA

    Srinivas Aluru

  3. Grid Computing and Distributed Systems Laboratory and, NICTA Victoria Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia

    Rajkumar Buyya

  4. Department of Computer Science, National Tsing Hua University, Taiwan

    Yeh-Ching Chung

  5. Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA, 71272, USA

    Sumeet Dua  & Vir V. Phoha  & 

  6. Department of Computer Sciences, Purdue University, W. Lafayette, IN, 47907, USA

    Ananth Grama

  7. Arizona State University, Tempe, AZ, 85281, USA

    Sandeep K. S. Gupta

  8. Computer Science and Engineering Department, Indian Institute of Technology Kharagpur, 721 302, WB, India

    Rajeev Kumar

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© 2009 Springer-Verlag Berlin Heidelberg

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Bajpai, A., Pathangay, V. (2009). Text and Language-Independent Speaker Recognition Using Suprasegmental Features and Support Vector Machines. In: Ranka, S., et al. Contemporary Computing. IC3 2009. Communications in Computer and Information Science, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03547-0_29

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  • DOI: https://doi.org/10.1007/978-3-642-03547-0_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03546-3

  • Online ISBN: 978-3-642-03547-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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