Comparison of Text-Dependent Speaker Identification Methods for Short Distance Telephone Lines Using Artificial Neural Networks

The transition to democracy in South Africa has brought with it certain challenges. The main challenge is to get rid of crime and corruption. This paper presents a technique to combat white-collar crime in telephone transactions by identifying and verifying speakers using Artificial Neural Networks (ANNs). Results are presented to show that speaker identification is feasible and this is illustrated with two different types of ANN architectures and with two different types of characteristic features as inputs to ANNs.