Perceptually inspired features for speaker likability classification

In this paper, we present a novel approach to the classification of speaker likability, that is, a measure of how pleasant a given speaker is to listen to. Instead of blindly extracting a large number of features, we identify a small set of features which represent perceptual speech characteristics. This set of features is sent to a linear support vector machine to perform speaker likability classification. We train and evaluate the performance of our algorithm on the Interspeech 2012 speaker trait challenge database and we show that our likability classifier achieves an absolute improvement of 3.2% over the baseline classifier developed for the challenge while considerably reducing the number of features needed.