Commenced in January 2007
Paper Count: 30169
Intelligent Speaker Verification based Biometric System for Electronic Commerce Applications
Abstract:Electronic commerce is growing rapidly with on-line sales already heading for hundreds of billion dollars per year. Due to the huge amount of money transferred everyday, an increased security level is required. In this work we present the architecture of an intelligent speaker verification system, which is able to accurately verify the registered users of an e-commerce service using only their voices as an input. According to the proposed architecture, a transaction-based e-commerce application should be complemented by a biometric server where customer-s unique set of speech models (voiceprint) is stored. The verification procedure requests from the user to pronounce a personalized sequence of digits and after capturing speech and extracting voice features at the client side are sent back to the biometric server. The biometric server uses pattern recognition to decide whether the received features match the stored voiceprint of the customer who claims to be, and accordingly grants verification. The proposed architecture can provide e-commerce applications with a higher degree of certainty regarding the identity of a customer, and prevent impostors to execute fraudulent transactions.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1054921Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1357
 A. J. Harris and D. C. Yen, Biometric authentication: assuring access to information, Information Management & Security 10/1, pp. 12-19, 2002.
 J.L. Dugelay, J.C. Junqua, C. Kotropoulos, and R. Kuhn, Recent Advantages in Biometric Person Authentication, ICASSP 2002, International Conference on Acoustics, Speech and Signal Processing, May 13, 2002, Orlando, Florida, USA.
 J. Ashbourn, Biometrics: advanced identity verification: The complete guide, Springer-Verlag, London, 2000.
 A. Klosterman and G. Ganger, Secure continuous biometric-enhanced authentication, Carnegie Mellon University, Pittsburgh, PA.
 L. R. Rabiner, A Tutorial on Hidden Markov Models and selected applications in Speech Recognition, Proc. IEEE, vol. 77, pp. 257-286, Feb. 1989.
 R. J. Mammone, X. Zhang and R. P. Ramachandran, Robust Speaker Recognition, A Feature-Based Approach, IEEE Signal Processing Magazine, 13 (5), September 1996, 55-71.
 J. P. Campbell, Speaker Recognition: A Tutorial, Proceedings of the IEEE, 85(9), September 1997, 1437-1462.
 L. Rabiner, BH Juang, Fundamentals of Speech Recognition, (Prentice Hall, 1993).
 S. Furui, Cepstral Analysis technique for automatic speaker verification, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-29, 1981.
 J.R. Deller, J.G.Proakis, and J.H.L.Hansen, Discrete-Time Processing of Speech Signals, Macmillan 1993.
 D. Reynolds, Speaker Identification and Verification using Gaussian Mixture speaker models, Speech Communications, vol 17, pp. 91-108, 1995.
 S. Navanati, M. Thieme, and R. Navanati, Biometrics: Identify verification in a networked world (John Wiley & Sons, Inc. 2002.
 Hynek Hermansky, Exploring Temporal Domain for Robustness in Speech Recognition, 15th International Congress on Acoustics, 1995.