Though parents regularly remind their children not to do so, talking while eating is a typical everyday situation automatic speech analysis systems should be able to deal with. The Paralinguistic Eating Condition (EC) Challenge at Interspeech 2015 sets the task to classify whether a speaker is eating or not, and if so, which type of food the speaker is currently tasting. The approach we follow in this paper is rather unusual: instead of suppressing the influence of noise to enhance the intelligibility of a spoken message, we try to emphasize the noisy parts of the spectrum to improve the recognition of food classes. To allow for a fine-grained adaption to the characteristic spectrum of single food types we adopt a hierarchical tree structure and decompose the classification task into a sequence of binary decisions. At each node we apply frequency-dependent weighting to tune the spectrum to the involved target classes. With our approach we are able to improve results in a 7-class recognition problem (6 types of food and no food) by more than 7% on the training set (using leave-one-eater-out cross validation) and 4% on the test set, respectively.
Bibliographic reference. Wagner, Johannes / Seiderer, Andreas / Lingenfelser, Florian / André, Elisabeth (2015): "Combining hierarchical classification with frequency weighting for the recognition of eating conditions", In INTERSPEECH-2015, 889-893.