Abstract:  Traditional speaker recognition frameworks are mostly based on the Gauss mixture model (GMM), but this shallow learning model can not effectively represent the high-order correlation between data features, thus the recognition effect is poor. In this paper, a speaker recognition method based on Deep Neural Network (DNN) and Pitch Period (PP) is proposed. The logarithmic Meier filter bank feature parameters are used as the input of DNN for mainline identification, and the voiceprint characteristics of the speaker are extracted through training DNN model. To eliminate the subjective influence of threshold setting in DNN model, dynamic time warping technology is used to match pitch period of the speaker for assistant recognition. The experimental results show that equal error rate (EER) of this dual recognition method reaches 1.6%, which decreases respectively by 1.2% and 2.4% compared with DNN system and EM-GMM system, and this method still has good robustness in noise environment.

Key words: deep neural network, pitch period, speaker recognition, dynamic time warping, dual recognition