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Speaker Dependent Frequency Cepstrum Coefficients

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 58))

Abstract

This paper aims at speaker recognition based upon a novel set of features. Feature extraction is a crucial phase of the speaker recognition process and a proper feature set can influence it dramatically. Many well-known features are not suitable for the speaker recognition as those merge the specifics of the individual voices to make them universal. Therefore, we need features accentuating the individual differences of our voices to be able to recognise speakers reliably. This paper introduces Speaker Dependent Frequency Cepstrum Coefficients (SDFCC) intended for the speaker recognition purposes only. Experimental results prove increase of the reliability in comparison to the well-known features. According to the test results, the SDFCC are very useful and promising for the speaker recognition.

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References

  1. Rodman, D.R.: Computer Speech Technology. Artech House, Boston (1999)

    Google Scholar 

  2. Sigmund, M.: Speaker Normalization by Long-Time Spectrum. In: Proceedings of Radioelektronika 1996, Brno, CZ, pp. 144–147 (1996)

    Google Scholar 

  3. Oppenheim, A.V., Schafer, R.W., Buck, J.R.: Discrete-Time Signal Processing, 2nd edn. Prentice Hall, Upper Saddle River (1999)

    Google Scholar 

  4. Sigmund, M.: Estimation of Vocal Tract Long-Time Spectrum. In: Proceedings of Elektronische Sprachsignalverarbeitung, Dresden, vol. 9, pp. 190–192 (1998)

    Google Scholar 

  5. Sigmund, M.: Speaker Recognition – Identifying People by their Voices. Conferment thesis FEE BUT, Brno (2000) ISBN 80-214-1590-8

    Google Scholar 

  6. Markel, J.D., Gray, A.H.: Linear Prediction of Speech. Springer, New York (1976)

    MATH  Google Scholar 

  7. Xafopoulos, A.: Speaker Verification. Tampere International Center for Signal Processing, TUT, Tampere, Finland (2001)

    Google Scholar 

  8. Baggenstoss, P.M.: Hidden Markov Models Toolbox. Naval Undersea Warfare Centre, Newport, RI (2001)

    Google Scholar 

  9. Woodward, J.D., Orlans, N.M., Higgins, P.T.: Biometrics: Identity Assurance in the Information Age. McGraw-Hill/Osborne, Berkley (2003)

    Google Scholar 

  10. Orsag, F.: Biometric Security Systems – Speaker Recognition Technology. Dissertation, Brno, CZ (2004)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Orság, F. (2009). Speaker Dependent Frequency Cepstrum Coefficients. In: Ślęzak, D., Kim, Th., Fang, WC., Arnett, K.P. (eds) Security Technology. SecTech 2009. Communications in Computer and Information Science, vol 58. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10847-1_32

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  • DOI: https://doi.org/10.1007/978-3-642-10847-1_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10846-4

  • Online ISBN: 978-3-642-10847-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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