Recognizing emotions in dialogues with acoustic and lexical features

Automatic emotion recognition has long been a focus of Affective Computing. We aim at improving the performance of state-of-the-art emotion recognition in dialogues using novel knowledge-inspired features and modality fusion strategies. We propose features based on disfluencies and nonverbal vocalis...

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Bibliographic Details
Main Authors: Leimin Tian, Moore, Johanna D., Lai, Catherine
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
Computation
Conferences
Context modeling
dialogue system
disfluency
Emotion recognition
Emotions
Feature extraction
Feature recognition
Predictive models
Recognition
State of the art
Strategy
Visualization
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Description
Summary:Automatic emotion recognition has long been a focus of Affective Computing. We aim at improving the performance of state-of-the-art emotion recognition in dialogues using novel knowledge-inspired features and modality fusion strategies. We propose features based on disfluencies and nonverbal vocalisations (DIS-NVs), and show that they are highly predictive for recognizing emotions in spontaneous dialogues. We also propose the hierarchical fusion strategy as an alternative to current feature-level and decision-level fusion. This fusion strategy combines features from different modalities at different layers in a hierarchical structure. It is expected to overcome limitations of feature-level and decision-level fusion by including knowledge on modality differences, while preserving information of each modality.
ISSN:2156-8111
DOI:10.1109/ACII.2015.7344651