Human Speaker Recognition Based on the Integration of Genetic Algorithm and RBF Network

Although the human speaker recognition system based on RBF network is one of the main models for recognizing speakers, it has some shortcomings, this is, the hidden layer node number of RBF network is often hard to be assigned, and the system has a slow convergence speed. In this paper, a RBF network optimization scheme based on genetic algorithm for human speaker recognition is proposed. In the scheme, the hybrid encoding genetic algorithm is used to optimize the connected weights and structure of RBF network and the redundant nodes and redundant connected weights are removed from the network effectively. The scheme utilizes the parallelism of the neural network and the global search capability of the genetic algorithm, so it improves the processing capability of the network evidently. The experimental tests show that the scheme based on hybrid encoding genetic algorithm has a fast learning speed, a high recognition rate, and it is a new practical scheme for human speaker recognition.