Robust spectral features for emotion recognition using GMM and SVM with PCA

Abstract

In this paper, the research work investigated on various spectral accents, for example, M.F.C.C, pitch-chroma, skew-ness, and centroid for feeling acknowledgment. For the test arrangement, the feelings considered in this investigation are Fear, Anger, Neutral, and Happy. The framework is assessed for different blends of spectral accents. At last, it makes sense of the blend of MFCC and skewness gave a superior acknowledgment execution when contrasted with different mixes. The previously mentioned accents are inspected utilizing Gaussian Mixture models (G.M.M.s) and Support Vector Machines (S.V.M.s). To expand the framework execution and evacuate insignificant data shape the recently produced vigorous accents, in this paper investigated an approach, namely Principal Component Analysis (PCA) is utilized to expel high dimensional information. It was set up that the acknowledgment execution for include sets in the wake of applying PCA got expanded in both grouping models utilizing GMMs and SVMs. The general framework is perceived 35% preceding PCA 58.3% later than PCA utilizing GMMs, and 28% preceding PCA, 50.5% later than PCA utilizing SVMs. The database utilized as a part of this examination is Telugu feeling speech corpus (IIT-KGP).