INTERSPEECH 2014
15th Annual Conference of the International Speech Communication Association

Singapore
September 14-18, 2014

Detecting the Intensity of Cognitive and Physical Load Using AdaBoost and Deep Rectifier Neural Networks

Gábor Gosztolya, Tamás Grósz, Róbert Busa-Fekete, László Tóth

MTA-SZTE RGAI, Hungary

The Interspeech ComParE 2014 Challenge consists of two machine learning tasks, which have quite a small number of examples. Due to our good results in ComParE 2013, we considered AdaBoost a suitable machine learning meta-algorithm for these tasks, besides we also experimented with Deep Rectifier Neural Networks. These differ from traditional neural networks in that the former have several hidden layers, and use rectifier neurons as hidden units. With AdaBoost we achieved competitive results, whereas with the neural networks we were able to outperform baseline SVM scores in both Sub-Challenges.

Full Paper

Bibliographic reference.  Gosztolya, Gábor / Grósz, Tamás / Busa-Fekete, Róbert / Tóth, László (2014): "Detecting the intensity of cognitive and physical load using AdaBoost and deep rectifier neural networks", In INTERSPEECH-2014, 452-456.