Although the human speaker recognition system based on back propagation（BP） neural networks is one of the main models for distinguishing speaker

it has some shortcomings

this is

the hidden layer node number of BP network is often hard to be assigned

and the system has a slow convergence speed.A BP neural networks optimization scheme based on genetic algorithm（GA） for human speaker recognition is proposed.In the scheme

the hybrid encoding GA is used to optimize the connected weights and structure of BP neural networks and the redundant nodes and redundant connected weights are removed from the networks effectively.The scheme utilizes the parallelism of the neural networks and the global search capability of the GA

so it improves the processing capability of the networks evidently.The experimental tests show that the scheme based on hybrid encoding GA has a fast learning speed

a high recognition rate

and it is a new practical scheme for human speaker recognition.