@InProceedings{Hübner2009_289,
<br/>   author = {David Hübner and Ronald Böck and Andreas Wendemuth},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2009},
<br/>   title = {Applications of HMMs for the Recognition of Emotional Sequences in the Valence-Arousal Space},
<br/>   year = {2009},
<br/>   editor = {Rüdiger Hoffmann},
<br/>   month = mar,
<br/>   pages = {200--206},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {This paper will show how models can be generated, which are capable
of recognizing sequences of emotional states from speech. For this purpose Hidden
Markov Models (HMMs) are introduced, which are trained on spontaneous, nonacted
emotions. Unlike other publications in this area, whose main focus is often on
the classification in one of the basic classes introduced by Ekman or Plutchik, we
will generate 2-dimensional representations of the user’s emotion in the Valence-
Arousal space. Hence, not only the basic emotions are recognized, but also an
additional parameter, the word frequency, is extracted from the speech signal. We
trained two gender specific models and one combined model and tested these afterwards
on unknown data. The evaluation of the robustness is done by using two
cross-validation methods.},
<br/>   isbn = {978-3-941298-31-6},
<br/>   issn = {0940-6832},
<br/>   keywords = {Sprachsynthese und Emotionsmodellierung},
<br/>   url = {https://www.essv.de/pdf/2009_200_206.pdf},
<br/>}