Abstract: There are still many shortcomings in the existing speaker recognition methods.The end-to-end method based on utte-rance-level features requires to process the input to be the same size due to the inconsistency of the speech length.The two-stage method of feature training with posterior classification makes the recognition system too complex.These factors affect the performance of the model.This paper proposed an end-to-end speaker recognition method based on frame-level features.The model uses frame-level speech as input,and the same size frame-level features effectively solve the problem of inconsistent speech-level speech input length,and the frame-level features can retain more speaker information.Compared with the mainstream two-stage identification system,the end-to-end identification method integrates feature training and classification,which simplifies the complexity of the model.During the training phase,each speech is segmented into multiple frame-level speech inputs to a Convolutional Neural Network (CNN) for training the model.In the evaluation phase,the trained CNN model classifies the frame-level speech,and each segment of speech calculates the prediction category of the speech data based on the prediction scores of multiple frames.The maximum predicted category of each frame and the average prediction value of each frame are adopted to calculate the class of each segment of speech respectively.In order to verify the validity of the work,the speech data of the Mandarin Emotio-nal Speech Corpus (MASC) were used for training and testing.The experimental results show that the end-to-end recognition method based on frame-level features achieves better performance than the existing methods.

Key words: Convolutional Neural Networks, End-to-end, Frame-level features, Speaker recognition, Utterance-level speech