Adaptation of ann for FPGA implementation and its application for speaker identification

Abstract

Speaker identification is a challenging pattern classification task. It is used enormously in many applications such as security systems, information retrieved services, etc. portable identification systems are expected to be widely used in future in many purposes, such as mobile applications. Implementing the identification technique using a dedicated hardware could be very useful to achieve smart units. In this context, the FPGA could offer an efficient technology to realize a pattern classification strategy. A speaker identification system can be implemented using many classification approaches, one of these , the artificial neural network (ANN), which is considered one of the most powerful classification techniques. Implementing a Neural Network on. an FPGA is a challenging task because of the complexity of the required arithmetic operations. In this paper the nonlinear activation function is adapted to be more suitable for the FPGA implementation. We have reached almost 100% identification rate using Multi Layer Perceptron Neural Network ( MLP NN).