16th Annual Conference of the International Speech Communication Association

Dresden, Germany
September 6-10, 2015

Automatic Classification of Eating Conditions from Speech Using Acoustic Feature Selection and a Set of Hierarchical Support Vector Machine Classifiers

Abhay Prasad, Prasanta Kumar Ghosh

Indian Institute of Science, India

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.

Full Paper

Bibliographic reference.  Prasad, Abhay / Ghosh, Prasanta Kumar (2015): "Automatic classification of eating conditions from speech using acoustic feature selection and a set of hierarchical support vector machine classifiers", In INTERSPEECH-2015, 884-888.