NDLI: A hierarchical framework for modeling multimodality and emotional evolution in affective dialogs

Content Provider	IEEE Xplore Digital Library
Author	Metallinou, A. Katsamanis, A. Narayanan, S.
Copyright Year	2012
Description	Author affiliation: Signal Analysis and Interpretation Lab (SAIL), University of Southern California, Los Angeles, USA (Metallinou, A.; Katsamanis, A.; Narayanan, S.)
Abstract	Incorporating multimodal information and temporal context from speakers during an emotional dialog can contribute to improving performance of automatic emotion recognition systems. Motivated by these issues, we propose a hierarchical framework which models emotional evolution within and between emotional utterances, i.e., at the utterance and dialog level respectively. Our approach can incorporate a variety of generative or discriminative classifiers at each level and provides flexibility and extensibility in terms of multimodal fusion; facial, vocal, head and hand movement cues can be included and fused according to the modality and the emotion classification task. Our results using the multimodal, multi-speaker IEMOCAP database indicate that this framework is well-suited for cases where emotions are expressed multimodally and in context, as in many real-life situations.
Starting Page	2401
Ending Page	2404
File Size	288121
Page Count	4
File Format	PDF
ISBN	9781467300452
ISSN	15206149
e-ISBN	9781467300469
e-ISBN	9781467300445
DOI	10.1109/ICASSP.2012.6288399
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-25
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Context Context modeling Emotion recognition Face Databases Vectors emotion recognition hierarchical HMM multimodality dialog modeling discriminative training
Content Type	Text
Resource Type	Article

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