Speaker identification and verification over short distance telephone l

SPEAKER IDENTIFICATION AND VERIFICATION OVER SHORT DISTANCE TELEPHONE LINES USING ARTIFICIAL NEURAL NETWORKS Ganesh K Venayagamoorthy, Narend Sunderpersadh, and Theophilus N Andrew gkumar@ieee.org sundern@telkom.co.za theo@wpo.mlsultan.ac.za Electronic Engineering Department, M L Sultan Technikon, P O Box 1334, Durban, South Africa. ABSTRACT Crime and corruption have become rampant today in our society and countless money is lost each year due to white collar crime, fraud, and embezzlement. This paper presents a technique of an ongoing work to combat white-collar crime in telephone transactions by identifying and verifying speakers using Artificial Neural Networks (ANNs). Results are presented to show the potential of this technique. 1. INTRODUCTION Several countries today are facing rampant crime and corruption. Countless money is lost each year due to white collar crime, fraud, and embezzlement. In today’s complex economic times, businesses and individuals are both falling victims to these devastating crimes. Employees embezzle funds or steal goods from their employers, then disappear or hide behind legal issues. Individuals can easily become helpless victims of identity theft, stock schemes and other scams that rob them of their money White collar crime occurs in the gray area where the criminal law ends and civil law begins. Victims of white collar crimes are faced with navigating a daunting legal maze in order to effect some sort of resolution or recovery. Law enforcement is often too focused on combating â€Å"street crime† or does not have the expertise to investigate and prosecute sophisticated fraudulent acts. Even if criminal prosecution is pursued, a criminal conviction does not mean that the victims of fraud are able to recover their losses. They have to rely on th criminal courts awarding restitution after the conviction and by then the perpetrator has disposed of or hidde most of the assets available for recovery. From the civil law perspective, resolution and recovery can just be a difficult as pursuing criminal prosecution. Perpetrators of white collar crime are often difficult to locate and served with civil process. Once the perpetrators have been located and served, proof must be provided that the fraudulent act occurred and recovery/damages are needed. This usually takes a lengthy legal fight, which often can cost the victim more money than t... ...phone speech†, IEEE Signal Processing Letters, vol. 2 no. 3 March 1995, pp. 46 - 48. [2] J.M.Naik, L.P.Netsch, G.R.Doddington, â€Å"Speaker verification over long distance telephone lines†, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 23-26 May 1989, pp. 524 - 527. [3] A.L.Mcilraith, H.C.Card, â€Å"Birdsong Recognition Using Backpropagation and Multivariate Statistics†, Proceedings of IEEE Trans on Signal Processing, vol. 45, no. 11, November 1997. [4] G.K.Venayagamoorthy, V.Moonasar, K.Sandrasegaran, â€Å"Voice Recognition Using Neural Networks†, Proceedings of IEEE South African Symposium on Communications and Signal Processing (COMSIG 98), 7-8 September 1998, pp. 29 - 32. [5] V.Moonasar, G.K.Venayagamoorthy, â€Å"Speaker identification using a combination of different parameters as feature inputs to an artificial neural network classifier†, accepted for publication in the Proceedings of IEEE Africon 99 conference, Cape Town, 29 September – 2 October 99. [6] H.Demuth, M.Beale, MATLAB Neural Network Toolbox User’s Guide, The Maths Works Inc., 1996. [7] T.Kohonen, Self-organizing and associate memory Spring Verlag, Berlin, third edition, 1989.