Hierarchical neural networks and enhanced class posteriors for social signal classification

With the impressive advances of deep learning in recent years the interest in neural networks has resurged in the fields of automatic speech recognition and emotion recognition. In this paper we apply neural networks to address speaker-independent detection and classification of laughter and filler vocalizations in speech. We first explore modeling class posteriors with standard neural networks and deep stacked autoencoders. Then, we adopt a hierarchical neural architecture to compute enhanced class posteriors and demonstrate that this approach introduces significant and consistent improvements on the Social Signals Sub-Challenge of the Interspeech 2013 Computational Paralinguistics Challenge (ComParE). On this task we achieve a value of 92.4% of the unweighted average area-under-the-curve, which is the official competition measure, on the test set. This constitutes an improvement of 9.1% over the baseline and is the best result obtained so far on this task.