Speaker identification using convolutional-long short-term memory neural networks

Abstract

Speaker identification (SI) techniques has been used in numerous commercial products over the last decades. In SI, the main purpose is to match a voice sample from an unknown speaker to one of the labeled speaker models. To be able accomplish this task, there are two operational phases, training (can be also termed as enrollment) and testing. In both phases, feature extraction and feature matching are the two key steps. In this work, we have extracted features with Mel-Frequency Cepstrum Coefficients (MFCC) because MFCC has accurate representation of the vocal tract, and a Spectral-Flux based Voice Activity Detector (VAD) is implemented to extract features from the speech segments. In feature matching task, we build a Convolutional Long short-term memory (LSTM) Neural Network for the speaker models. We examine main performance of the system in terms of identification rate and compare the proposed method with other SI methods under several noisy conditions at different signal to noise ratio (SNR) levels.Speaker identification (SI) techniques has been used in numerous commercial products over the last decades. In SI, the main purpose is to match a voice sample from an unknown speaker to one of the labeled speaker models. To be able accomplish this task, there are two operational phases, training (can be also termed as enrollment) and testing. In both phases, feature extraction and feature matching are the two key steps. In this work, we have extracted features with Mel-Frequency Cepstrum Coefficients (MFCC) because MFCC has accurate representation of the vocal tract, and a Spectral-Flux based Voice Activity Detector (VAD) is implemented to extract features from the speech segments. In feature matching task, we build a Convolutional Long short-term memory (LSTM) Neural Network for the speaker models. We examine main performance of the system in terms of identification rate and compare the proposed method with other SI methods under several noisy conditions at different signal to noise ratio (SNR) levels.