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This paper presents the novel Cascaded acoustic Group and Individual Feature Selection (CGI-FS) method for automatic recognition of food likability rating addressed in the ICMI 2018 Eating Analysis and Tracking Challenge’s Likability Sub-Challenge. Employing the speech and video recordings of the iHEARu-EAT database, the Likability Sub-Challenge attempts to recognize self-reported binary labels, ‘Neutral’ and ‘Like’, assigned by subjects to food they consumed while speaking. CGI-FS uses an audio approach and performs a sequence of two feature selection operations by considering the acoustic feature space first in groups and then individually. In CGI-FS, an acoustic group feature is defined as a collection of features generated by the application of a single statistical functional to a specified set of audio low-level descriptors. We investigate the performance of CGI-FS using four different classifiers and evaluate the relevance of group features to the task. All four CGI-FS system results outperform the Likability Sub-Challenge baseline on iHEARu-EAT development data with the best performance achieving a 9.8% relative Unweighted Average Recall improvement over it.

Cascaded Acoustic Group and Individual Feature Selection for Recognition of Food Likability