SPEAKER RECOGNITION SYSTEM USING ARTIFICIAL NEURAL NETWORKS

SPEAKER RECOGNITION SYSTEM USING ARTIFICIAL NEURAL NETWORKS

ABSTRTACT:

Many people today have access to their company’s information system by logging in from home. Also Internet services and telephone banking are widely used by corporate and private sectors. Therefore to protect one’s resources or information with simple password is not reliable and secure in the world of today. Biometrics are methods for recognizing a user based upon his/her unique physiological and/or behavioral characteristics. Voice signal as unique behavioral characteristics is presented in this paper for speaker verification over telephone lines using artificial neural network (ANN) for banking application. Here Multi-layer feed forward artificial neural network (ANN) system capable of verifying a speaker among the group of speakers is designed. Spectral density of recorded voice signal is used for characterization. Finally the feasibility of the speaker recognition system is tested. This system found more efficient in speaker recognition.

INTRODUCTION:

There is a vital need for speaker identification in all spheres of life. The most important being that this system will enable people to have secure access to information and property. It has significant advantage that in electronic banking and Internet access. 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 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.

One solution to avoid such white-collar crimes and shorten the lengthy time in locating and serving perpetrators with a judgment is by use of biometrics techniques for verifying individuals. Artificial neural network (ANN) are intelligence systems that are related in some way to a simplified biological model of human brain. Attenuation and distortion of voice signals exists over telephone lines and artificial neural network, despite a nonlinear, noisy and un -stationary environment, is still good at recognizing and verifying unique characteristics of signal such as speech. Speaker recognition involves speaker identification or speaker verification based on his\her voice in the form of speech.


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