Article: Emotion recognition using MLP and GMM for Oriya language Journal: International Journal of Computational Vision and Robotics (IJCVR) 2017 Vol.7 No.4 pp.426 - 442 Abstract: Emotion recognition of human beings is one of the major challenges in the modern complicated world of political and criminal scenario. In this paper an attempt is taken to recognise two classes of speech emotions as high arousal like angry, surprise and low arousal like sad and bore. Linear prediction coefficients (LPC), Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features are used for emotions recognition using multilayer perceptron (MLP) and Gaussian mixture model (GMM) classifier. Two different databases of four emotions, one of five children and other one of a professional actor has been used in this work. Emotion recognition performance of LPC, PLP and MFCC features has been compared with two classifiers, MLP and GMM. MFCC features with MLP classifier and PLP features with GMM classifier has performed best in their respective categories. Inderscience Publishers - linking academia, business and industry through research

Title: Emotion recognition using MLP and GMM for Oriya language

Authors: Hemanta Kumar Palo; Mahesh Chandra; Mihir Narayan Mohanty

Addresses: Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Jagamara, Bhubaneswar, Odisha, India ' Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India ' Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Jagamara, Bhubaneswar, Odisha, India

Abstract: Emotion recognition of human beings is one of the major challenges in the modern complicated world of political and criminal scenario. In this paper an attempt is taken to recognise two classes of speech emotions as high arousal like angry, surprise and low arousal like sad and bore. Linear prediction coefficients (LPC), Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features are used for emotions recognition using multilayer perceptron (MLP) and Gaussian mixture model (GMM) classifier. Two different databases of four emotions, one of five children and other one of a professional actor has been used in this work. Emotion recognition performance of LPC, PLP and MFCC features has been compared with two classifiers, MLP and GMM. MFCC features with MLP classifier and PLP features with GMM classifier has performed best in their respective categories.

Keywords: emotion recognition; Mel-frequency cepstral coefficient; MFCC; linear prediction coefficients; LPCs; perceptual linear prediction; PLP; neural network; multilayer perceptron; MLP; radial basis function; Gaussian mixture model; GMM.

DOI: 10.1504/IJCVR.2017.084987

International Journal of Computational Vision and Robotics, 2017 Vol.7 No.4, pp.426 - 442

Received: 02 Mar 2015
Accepted: 20 May 2015
Published online: 10 Jul 2017 *

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Title: Emotion recognition using MLP and GMM for Oriya language

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