This work studies automatic recognition of paralinguistic properties of speech. The focus is on selection of the most useful acoustic features for three classification tasks: 1) recognition of autism spectrum developmental disorders from child speech, 2) classification of speech into different affective categories, and 3) recognizing the level of social conflict from speech. The feature selection is performed using a new variant of random subset sampling methods with k-nearest neighbors (kNN) as a classifier. The experiments show that the proposed system is able to learn a set of important features for each recognition task, clearly exceeding the performance of the same classifier using the original full feature set. However, some effects of overfitting the feature sets to finite data are also observed and discussed.
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