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Voice-Pishing Detection Algorithm Based on 3GPP2 SMV  

Lee, Kye-Hwan (Department of Electronics Engineering Inha University)
Chang, Joon-Hyuk (Department of Electronics Engineering Inha University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.45, no.4, 2008 , pp. 92-99 More about this Journal
Abstract
We propose an effective voice-pishing detection algorithm based on the 3GPP2 selectable mode vocoder (SMV). The detection of voice pishing is performed based on a Gaussian mixture model (GMM) using decoding parameters of the SMV directly extracted from the decoding process of the transmitted speech information in the mobile phone. The experimental results indicate that SMV decoding parameters are effective in discriminating between general voice and phisher's voice and the performance is significantly acceptable when the proposed technique is applied.
Keywords
Selectable Mode Vocoder (SMV); Gaussian Mixture Model (GMM);
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1 Greer S. C., and Dejaco A., "Standardization of the selectable mode vocoder," IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 953-956, 2001
2 3GPP2 Spec., "Software distribution for selectable mode vocoder (SMV), service option 56, specification," 3GPP2-C. Roo30-0, v3.0, 2005
3 Harry H., and James D. H., "Voice Stress Analyzer Instrumentation Evaluation," Final Report for Department of Defense Counterintelligence Field Activity Contract-FA 4814-04-0011, 2006
4 Gaunard P., Mubibanqiey C. G., Couvreur C., and Fontaine V., "Automatic classification of environmental noise events by hidden Markov models," IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3609-3612, 1998
5 Muthusamy Y. K., Cole R. A., and Oshika B. T., "The OGI multi-language telephone speech corpus," International Conference on Spoken language Processing, vol. 2, pp. 895-989, 1992
6 Kabal P., Prakash R., and Ramachandran, "The computation of line spectral frequencies using Chebyshev polynomials," IEEE trans. Acoustics, Speech, and Signal Processing, vol. ASSP-34, no. 6, pp. 1419-1426, 1986
7 Ohmuro H., Moriya T., Mano K., and Miki S., "Coding of LSF parameters using interframe moving averate prediction and multi-stage vector quantization," IEEE Workshop on Speech Coding for Telecommunications, pp.63-64, 1993
8 Tsang-Long P., Yu-Te C., and Jun-Heng Y., "Emotion recognition from Mandarin speech signals," International Symposium on Chinese Spoken Language Processing, pp. 301-304, 2004
9 Gadallah M. E., Matar M. A., and Alqezawi A. F., "Speech based automatic lie detection," 16th National Radio Science Conference, C22/1-C33/8, 1999
10 Ekman P., Friesen W. V., and Scherer K., "Body movement and voice pitch in deceptive interaction," Semiotica, vol. 16, no. 1, pp. 23-27, 1976   DOI
11 uredly J. J., Davis C., and Gurevich M., "Differentiation of deception as a psychological process:A psychophysiological approach," Psychophysiology, vol. 25, no. 6, pp.683-688, 1988   DOI   ScienceOn
12 Dempster A. P., Land N. M., and Rubin D. B., "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society, Series B, vol. 39, no. 1, pp.1-38, 1977
13 Daniel N., Kjell E., and Kornel L., "Emotion Recognition in spontaneous speech using GMM," INTERSPEECH, pp. 809-812, 2006
14 Duda R. O., Hart P. E., and Stork D. G., Pattern classification, John Wiley & Sons, INC., 2001
15 Yang G., Shlomot E. B., Thyssen J., Huan-yu S., and Murgia C., "The SMV algorithm selected by TIA and 3GPP2 for CDMA applications," IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 709-712, 2001
16 Bishop C. M, Neural networks for pattern recognition, Oxford University Press, UK, 1995