Real life emotion classification using spectral features and gaussian mixture models

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Abstract

In this work, spectral features are extracted for speech emotion classification. Mel frequency cepstral coefficients (MFCCs) are used as features. Gaussian mixture models (GMMs) are explored as classifiers. The emotions considered are anger, happy, neutral, sad and surprise. Semi-natural emotional database (Graphic Era University Semi Natural Emotion Speech Corpus) is collected from the dialogues of popular Hindi movies. Average emotion recognition performance, in the case of multiple speaker database is observed to be around 55.60%. Results of male, female, multiple male and multiple female speakers are compared to study the effect of speakers and gender on expression of emotions. © 2012 Published by Elsevier Ltd.

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Koolagudi, S. G., Barthwal, A., Devliyal, S., & Rao, K. S. (2012). Real life emotion classification using spectral features and gaussian mixture models. In Procedia Engineering (Vol. 38, pp. 3892–3899). Elsevier Ltd. https://doi.org/10.1016/j.proeng.2012.06.447

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