Research on Speaker Identification Models Based on CNN and Additive Angular Margin Loss

In view of the traditional speaker identification methods which usually have poor generalization ability and high computational complexity, three text-independent closed-set speaker identification models named Res-SD, Res-SArc and Rep-SArc respectively are designed based on the Red-Song dataset constructed by us. Res-SD is trained by using the traditional cross-entropy loss, while Res-SArc and Rep-SArc are trained by using additive angular margin loss that optimizes the feature embedding to realize higher intra-class similarity and higher inter-class diversity. In terms of the number of model parameters, Rep-SArc accounts for only 1/3 of Res-SArc. Test experiments for evaluating three models are conducted on the Red-Song dataset's test set. In terms of identification accuracy, Rep-SArc and Res-SArc are more competitive among three models, and their identification accuracies reach 97.90% and 97.19%, respectively. The experimental results validate that these three models are effective for the text-independent closed-set speaker identification tasks.