Emotional speaker recognition in real life conditions using multiple descriptors and i-vector speaker modeling technique

Abstract

Emotional speaker recognition under real life conditions becomes an urgent need for several applications. This paper proposes a novel approach using multiple feature extraction methods and i-vector modeling technique in order to improve emotional speaker recognition under real conditions. The performance of the proposed approach is evaluated on real condition speech signal (IEMOCAP corpus) under clean and noisy environments using various SNR levels. We examined divers known spectral features in speaker recognition (MFCC, LPCC and RASTA-PLP) and performed combined features called MFCC-SDC coefficients. The feature vectors are then classified using the multiclass Support Vector Machines (SVM). Experimental results illustrate good robustness of the proposed system against talking conditions (emotions) and against real life environment (noise). Besides, results reveal that MFCC-SDC features outperforms the conventional MFCCs.