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.
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.