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

被引：0

作者：

Gosztolya, Gabor ^{[1
]}

Grosz, Tamas ^{[1
]}

Busa-Fekete, Robert ^{[1
]}

Toth, Laszlo ^{[1
]}

机构：

[1] MTA SZTE Res Grp Artificial Intelligence, Szeged, Hungary

来源：

15TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2014), VOLS 1-4 | 2014年

关键词：

speech technology; AdaBoost; deep neural networks; rectifier activation function;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：452 / 456

页数：5

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