Speaker Recognition of Fiber-Optic External Fabry-Perot Interferometric Microphone Based on Deep Learning

Abstract

In order to realize speaker recognition in flammable, explosive and strong electromagnetic interference environments, the speaker recognition technology of fiber-optic external Fabry-Perot interferometric (EFPI) microphone based on Deep learning is studied in this paper. The combination of convolutional neural network and long short-term memory (CNN-LSTM) model is proposed for speaker recognition in EFPI acoustic sensing system. The intensity self-compensation method is used to demodulate the voice signals of different speakers, then, the spectral features of voice signals are divided into training set and test set. The training set are input into the established CNN-LSTM model for model training, and the test set are input into the trained model for testing. The experimental results show that the CNN-LSTM model proposed in this paper has good training accuracy and test accuracy, reaching 100% and 94.0% respectively. Comparing with CNN and LSTM recurrent neural network (LSTM-RNN), the test accuracy of this model is 11.1% and 13.4% higher respectively.