To improve the feature extraction ability of speaker recognition and enhance the low recognition rate in noise environment,a speaker recognition algorithm—ASP-SERes2Net is proposed based on residual network.First,the Mel spectrum was used as the input of the neural network.Second,the residual block of the Res2Net was improved and squeeze-and-excitation(SE)attention module was introduced.Then,the average pooling was replaced by the attention statistics pooling(ASP).Finally,the additive angular margin Softmax(AAM-Softmax)function was used to classify the identity of the speaker.Through experiments,the performance of the ASP-SERes2Net algorithm was compared with that of time delay neural network(TDNN),ResNet34 and Res2Net.The minimum detection cost function(MinDCF)value of the ASP-SERes2Net algorithm was 0.040 1 and equal error rate(EER)was 0.52％,which were significantly better than the other three models.Results show that the ASP-SERes2Net algorithm has better performance and is suitable for speaker recognition applied in noise environment.
维普
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