Emotion recognition in speech using MFCC and wavelet features

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Krishna Kishore, K.V. and Krishna Satish, P. (2013) Emotion recognition in speech using MFCC and wavelet features. Proceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013. pp. 842-847. ISSN 978-1-4673-4527-9(ISBN)

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School: School of Computing > Department of Advanced Computer Science and Engineering

Department: School of Computing > Department of Advanced Computer Science and Engineering

Abstract

Recognition of emotions from speech is one of the most important sub domains in the field of affective computing.
Six basic emotional states are considered for classification of
emotions from speech in this work. In this work, features are
extracted from audio characteristics of emotional speech by Melfrequency Cepstral Coefficient (MFCC), and Subband based
Cepstral Parameter (SBC) method. Further these features are
classified using Gaussian Mixture Model (GMM). SAVEE audio
database is used in this work for testing of Emotions. In the
experimental results, SBC method out performs with 70% in
recognition compared to 51% of recognition in MFCC algorithm.

Item Type:	Article
Subjects:	AC Rearch Cluster
Depositing User:	Unnamed user with email techsupport@mosys.org
Date Deposited:	16 Dec 2023 06:49
Last Modified:	16 Dec 2023 06:53
URI:	https://ir.vignan.ac.in/id/eprint/575

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