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Nonlinear Predictive Models: Overview and Possibilities in Speaker Recognition

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Progress in Nonlinear Speech Processing

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4391))

Abstract

In this paper we give a brief overview of speaker recognition with special emphasis on nonlinear predictive models, based on neural nets. Main challenges and possibilities for nonlinear feature extraction are described, and experimental results of several strategies are provided. This paper is presented as a starting point for the non-linear model for speaker recognition.

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References

  1. Faundez-Zanuy, M.: On the vulnerability of biometric security systems. IEEE Aerospace and Electronic Systems Magazine 19(6), 3–8 (2004)

    Article  Google Scholar 

  2. Faundez-Zanuy, M.: Biometric recognition: why not massively adopted yet? IEEE Aerospace and Electronic Systems Magazine 20(8), 25–28 (2005)

    Article  Google Scholar 

  3. Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: The DET curve in assessment of detection performance. In: European speech Processing Conference Eurospeech, vol. 4, pp. 1895–1898 (1997)

    Google Scholar 

  4. Furui, S.: Digital Speech Processing, synthesis, and recognition. Marcel Dekker, New York (1989)

    Google Scholar 

  5. Campbell, J.P., Reynolds, D.A., Dunn, R.B.: Fusing high- and low-level features for speaker recognition. In: Eurospeech 2003, Geneva (2003)

    Google Scholar 

  6. Faundez-Zanuy, M.: Data fusion in biometrics. IEEE Aerospace and Electronic Systems Magazine 20(1), 34–38 (2005)

    Article  Google Scholar 

  7. Faundez-Zanuy, M., Monte-Moreno, E.: State-of-the-art in speaker recognition. IEEE Aerospace and Electronic Systems Magazine 20(5), 7–12 (2005)

    Article  Google Scholar 

  8. Reynolds, D.A., Rose, R.C.: Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Trans. On Speech and Audio Processing 3(1), 72–83 (1995)

    Article  Google Scholar 

  9. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  10. http://www.praat.org

  11. Ortega-García, J., González-Rodríguez, J., Marrero-Aguiar, V.: AHUMADA: A Large Speech Corpus in Spanish for Speaker Characterization and Identification. Speech communication 31, 255–264 (2000)

    Article  Google Scholar 

  12. Doddington, G.: Speaker Recognition based on Idiolectal Differences between Speakers. In: Eurospeech, vol. 4, Aalborg, pp. 2521–2524 (2001)

    Google Scholar 

  13. Manning, C.D., Schtze, H.: Foundations of Statistical Natural Language Processing, 1st edn. MIT Press, Cambridge (June 18, 1999)

    MATH  Google Scholar 

  14. Thyssen, J., Nielsen, H., Hansen, S.D.: Non-linear short-term prediction in speech coding. IEEE ICASSP, pp. I-185–I-188 (1994)

    Google Scholar 

  15. Townshend, B.: Nonlinear prediction of speech. In: IEEE ICASSP-1991, vol. 1, pp. 425–428 (1991)

    Google Scholar 

  16. Teager, H.M.: Some observations on oral air flow vocalization. IEEE trans. ASSP 82, 559–601 (1980)

    Google Scholar 

  17. Kubin, G.: Nonlinear processing of speech. In: Kleijn, W.B., Paliwal, K.K. (eds.) Speech coding and synthesis, Elsevier, Amsterdam (1995)

    Google Scholar 

  18. Thyssen, J., Nielsen, H., Hansen, S.D.: Non-linearities in speech. In: Proceedings IEEE workshop Nonlinear Signal & Image Processing, NSIP’95 (June 1995)

    Google Scholar 

  19. Faúndez-Zanuy, M.: Nonlinear Speech Processing: Overview and Possibilities in Speech Coding. In: Chollet, G., Esposito, A., Faúndez-Zanuy, M., Marinaro, M. (eds.) Nonlinear Speech Modeling and Applications. LNCS (LNAI), vol. 3445, pp. 15–42. Springer, Heidelberg (2005)

    Google Scholar 

  20. Kumar, A., Gersho, A.: LD-CELP speech coding with nonlinear prediction. IEEE Signal Processing letters 4(4), 89–91 (1997)

    Article  Google Scholar 

  21. Wu, L., Niranjan, M., Fallside, F.: Fully vector quantized neural network-based code-excited nonlinear predictive speech coding. IEEE transactions on speech and audio processing 2(4) (1994)

    Google Scholar 

  22. Wang, S., Paksoy, E., Gersho, A.: Performance of nonlinear prediction of speech. In: Proceedings ICSLP-1990, pp. 29–32 (1990)

    Google Scholar 

  23. Lee, Y.K., Johnson, D.H.: Nonparametric prediction of non-gaussian time series. In: IEEE ICASSP 1993, vol. IV, pp. 480–483 (1993)

    Google Scholar 

  24. Ma, N., Wei, G.: Speech coding with nonlinear local prediction model. In: IEEE ICASSP 1998, vol. II, pp. 1101–1104 (1998)

    Google Scholar 

  25. Pitas, I., Venetsanopoulos, A.N.: Non-linear digital filters: principles and applications. Kluwer Academic Publishers, Dordrecht (1990)

    Google Scholar 

  26. Lippmann, R.P.: An introduction to computing with neural nets. IEEE trans. ASSP 3(4), 4–22 (1988)

    Google Scholar 

  27. Jain, A.K., Mao, J.: Artificial neural networks: a tutorial. IEEE Computer, 31–44 (March 1996)

    Google Scholar 

  28. Faundez-Zanuy, M., Rodriguez, D.: Speaker recognition using residual signal of linear and nonlinear prediction models. In: 5th International Conference on spoken language processing (ICSLP’98), vol. 2, Sydney, pp. 121–124 (1998)

    Google Scholar 

  29. Faundez-Zanuy, M.: Speaker recognition by means of a combination of linear and nonlinear predictive models. In: EUROSPEECH’99, vol. 2, Budapest, pp. 763–766 (1999)

    Google Scholar 

  30. Soong, F.K., Rosenberg, A.E., Rabiner, L.R., Juang, B.H.: A vector quantization approach to speaker recognition. In: ICASSP 1985, pp. 387–390 (1985)

    Google Scholar 

  31. Gas, B., Zarader, J.L., Chavy, C., Chetouani, M.: Discriminant neural predictive coding applied to phoneme recognition. Neurocomputing 56, 141–166 (2004)

    Article  Google Scholar 

  32. Chetouani, M., Faúndez-Zanuy, M., Gas, B., Zarader, J.-L.: Non-linear Speech Feature Extraction for Phoneme Classification and Speaker Recognition. In: Chollet, G., Esposito, A., Faúndez-Zanuy, M., Marinaro, M. (eds.) Nonlinear Speech Modeling and Applications. LNCS (LNAI), vol. 3445, pp. 344–350. Springer, Heidelberg (2005)

    Google Scholar 

  33. Kleijn, W.B.: Signal processing representations of speech. IEICE Trans. Inf. And Syst. E86-D(3), 359–376 (2003)

    Google Scholar 

  34. Kolmogorov, A.N.: On the representation of continuous functions of several variables by superposition of continuous functions of one variable and addition. Dokl., 679–681 (1957)

    Google Scholar 

  35. Kurkova, V.: Kolmogorov’s theorem is relevant. Neural Computation 3(4), 617–622 (1991)

    Article  Google Scholar 

  36. Hecht-Nielsen, R.: Kolmogorov’s mapping neural network existence theorem. In: Proc. of International Conference on Neural Networks, pp. 11–13 (1987)

    Google Scholar 

  37. Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    Google Scholar 

  38. Gas, B., Chetouani, M., Zarader, J.L., Feiz, F.: The Predictive Self_Organizing Map: application to speech features extraction. In: WSOM’05 (2005)

    Google Scholar 

  39. Burrows, T.L.: Speech processing with linear and neural networks models. PhD Cambridge (1996)

    Google Scholar 

  40. Chetouani, M., Faundez-Zanuy, M., Gas, B., Zarader, J.L.: A new nonlinear speaker parameterization algorithm for speaker identification. In: Speaker Odyssey’04: Speaker Recognition Workshop, Toledo, Spain (May 2004)

    Google Scholar 

  41. Ross, A.A., Nandakumar, K., Jain, A.K.: Handbook of multibiometrics. Springer, Heidelberg (2006)

    Google Scholar 

  42. Faundez-Zanuy, M.: On-line signature recognition based on VQ-DTW. Pattern Recognition 40, 981–992 (2007)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Escola Universitària Politècnica de Mataró (BARCELONA), Spain, Université Pierre and Marie Curie, Paris VI, France

    Marcos Faundez-Zanuy & Mohamed Chetouani

Authors
  1. Marcos Faundez-Zanuy
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  2. Mohamed Chetouani
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Yannis Stylianou Marcos Faundez-Zanuy Anna Esposito

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Faundez-Zanuy, M., Chetouani, M. (2007). Nonlinear Predictive Models: Overview and Possibilities in Speaker Recognition. In: Stylianou, Y., Faundez-Zanuy, M., Esposito, A. (eds) Progress in Nonlinear Speech Processing. Lecture Notes in Computer Science, vol 4391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71505-4_10

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  • DOI: https://doi.org/10.1007/978-3-540-71505-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71503-0

  • Online ISBN: 978-3-540-71505-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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