14thAnnual Conference of the International Speech Communication Association

Lyon, France
August 25-29, 2013

Detecting Autism, Emotions and Social Signals Using AdaBoost

Gábor Gosztolya, Róbert Busa-Fekete, László Tóth

Hungarian Academy of Sciences, Hungary

In the area of speech technology, tasks that involve the extraction of non-lingustic information have been receiving more attention recently. The Computational Paralinguistics Challenge (ComParE 2013) sought to develop techniques to efficiently detect a number of paralinguistic events, including the detection of non-linguistic events (laughter and fillers) in speech recordings as well as categorizing whole (albeit short) recordings by speaker emotion, conflict or the presence of development disorders (autism). We treated these sub-challenges as general classification tasks and applied the general-purpose machine learning meta-algorithm, AdaBoost.MH, and its recently proposed variant, AdaBoost.MH.BA, to them. The results show that these new algorithms convincingly outperform baseline SVM scores.

Full Paper

Bibliographic reference.  Gosztolya, Gábor / Busa-Fekete, Róbert / Tóth, László (2013): "Detecting autism, emotions and social signals using adaboost", In INTERSPEECH-2013, 220-224.