Abstract
In this research we propose a new scheme for generating binary vectors, which can be used as keys for cryptographic purposes. These vectors are obtained from the speech signal and from the spoken user passphrase. The key bits are built using the Automatic Speech Recognition Technology to detect the phoneme limits in the speech utterance and the Support Vector Machines technique for classification. Linear prediction cepstral coefficients, (first and second derivatives) of the speech signal are calculated to create a 39-dimensional hyperspace. Then a hyperplane is created using an RBF kernel, and the SVM classifies the user’s phonemes. Applying our method to a set of 10, 20, 30 and 50 speakers from the YOHO database, the results show that this method is sufficiently robust to reliably regenerate the cryptographic key.
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References
Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory (1992)
Campbell Jr, J.P.: Features and Measures for Speaker Recognition. Ph.D. Dissertation, Oklahoma State University (1992)
Cortes, C., Vapnik, V.: Support-vector network. Machine Learning 20, 273–297 (1995)
Higgins, A., Porter, J., Bahler, L.: YOHO Speaker Authentication Final Report. ITT Defense Communications Division (1989)
Young, S.P.: Woodland HTK Hidden Markov Model Toolkit home page, http://htk.eng.cam.ac.uk/
Monrose, F., Reiter, M.K., Li, Q., Wetzel, S.: Cryptographic Key Generation From Voice. In: Proceedings of the IEEE Conference on Security and Privacy, Oakland, CA (May 2001)
Osuna, E., Freund, R., Girosi, F.: Support vector machines: Training and applications. Technical Report AIM-1602, MIT A.I. Lab (1996)
Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: An Application to Face Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 130–136 (1997)
Rabiner, L.R., Juang, B.-H.: Fundamentals of speech recognition. Prentice-Hall, New-Jersey (1993)
Joachims, T.: SVMLight: Support Vector Machine, SVM-Light Support Vector Machine, University of Dortmund (November 1999), http://svmlight.joachims.org/
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© 2005 Springer-Verlag Berlin Heidelberg
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García-Perera, P.L., Mex-Perera, C., Nolazco-Flores, J.A. (2005). Cryptographic-Speech-Key Generation Using the SVM Technique over the lp-Cepstral Speech Space. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science(), vol 3445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11520153_20
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DOI: https://doi.org/10.1007/11520153_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27441-4
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