Speaker identification system using Gaussian Mixture Model and Support Vector Machines (GMM-SVM) under noisy conditions

Abstract

Background: Automatic Speaker Identification (SID) systems has been a major breakthrough and crucial in many real-world applications. Methods: This work addresses the SID task based on GMM-SVM in a three stage process. Firstly, the Gammatone Frequency Cepstral Coefficients (GFCC) and Mean Hilbert Envelope Coefficients (MHEC) of the speakers are extracted. Secondly, these features are modeled using Gaussian Mixture Model (GMM), on adapting the extracted acoustic features by mean, the corresponding super vectors are found and these vectors are trained using Support Vector Machine (SVM). Finally, the actual recognition is done by feeding the super vectors of them asked noisy test utterance by Ideal Binary Mask (IBM) into SVM model and their accuracy of recognition is compared for GFCC, MHEC and RASTA-MFCC in different noisy conditions. Findings: Evaluation results show that SID performance carried out with MHEC is extensively better than the performance of other two features. Applications: Major areas that implements automatic SIDs are forensics, surveillance and audio biometrics etc.