Automatic classification of eating conditions from speech using acoustic feature selection and a set of hierarchical support vector machine classifiers

Prasad, Abhay and Ghosh, Prasanta Kumar (2016) Automatic classification of eating conditions from speech using acoustic feature selection and a set of hierarchical support vector machine classifiers. In: 16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015), SEP 06-10, 2015, Dresden, GERMANY, pp. 884-888.

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Abstract

The problem of automatic classification of seven types of eating conditions from speech is considered. Based on the confusion among different eating conditions from a seven class support vector machine (SVM) classifier, a hierarchical SVM classifier is designed. Experiments on the iHEARu-EAT database show that the hierarchical classifier results in a better classification accuracy compared to a seven class classifier. We also perform a feature selection for each of the classifiers in the hierarchical approach. This further improves the unweighted average recall (UAR) to 73.7% compared to an UAR of 60.9% obtained from the baseline scheme of a direct seven-way classification.

Item Type:	Conference Proceedings
Additional Information:	Copy right for this article belongs to the ISCA-INT SPEECH COMMUNICATION ASSOC, C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	03 Dec 2016 04:33
Last Modified:	03 Dec 2016 04:33
URI:	http://eprints.iisc.ac.in/id/eprint/55128

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