A speaker identification method based on deep learning

Abstract

This invention lies in the field of digital audio processing, which is a speech recognition system for identifying different identities based on deep learning. The invention consists of the following steps: First of all, the preparation of sufficient data was made, and the data was also split into training and testing data. Secondly, we preprocessed the data by using Voice Activity Detection (VAD) for detecting the effective audio segments, and Mel-frequency cepstral coefficients (MFCC) for feature extraction. Then, the training data were batched into the Convolutional Neural Network (CNN) that we have already set. Simultaneously, the parameters in the CNN were adjusted, namely dropout rate, learning base rate, loss rate, in order to optimize the performance of the model. Eventually, the optimal CNN can be used for the testing data, and the identities can be recognized with an accuracy of 92.6%. In brief, the identity of the speaker can be recognized automatically without human involvement by this invention. Raw Data Mono conversion Voice Activity Detection Audio Segment transform to .mat format Training Data Figure 1