@InProceedings{Schwenker2018_413,
<br/>   author = {Friedhelm Schwenker},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2018},
<br/>   title = {Multimodal Affect Classification Using Deep Neural Networks},
<br/>   year = {2018},
<br/>   editor = {André Berton and Udo Haiber and Wolfgang Minker},
<br/>   month = mar,
<br/>   pages = {240--246},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {Research activities in human-computer interaction increasingly ad-
dressed the aspect of integrating emotional intelligence into the overall system,
and therefore the recognition of human emotions becomes important in such appli-
cations. Human emotions are expressed through various kinds of modalities such
as voice, facial expressions, hand/body gestures or bio-physiological patterns, and
therefore the classification of human emotions should be considered as a multi-
modal pattern recognition and machine learning problem. In this paper we propose
artificial neural networks for the task of information fusion in multimodal affect
classification.},
<br/>   isbn = {978-3-959081-28-3},
<br/>   issn = {0940-6832},
<br/>   keywords = {Hauptvortrag},
<br/>   url = {https://www.essv.de/pdf/2018_240_246.pdf},
<br/>}