Recognizing Emotions in Dialogues With Disfluencies and Non-verbal Vocalisations
Year: 2015
Type: article
Abstract: We investigate the usefulness of DISfluencies and Non-verbal Vocalisations (DIS-NV) for recognizing human emotions in dialogues. The proposed features measure filled pauses, fillers, stutters, laughter, and breath in utterances. The predictiveness of DIS-NV features is compared with lexical features and state-of-the-art low-level acoustic features. Our experimental results show that using DIS-NV features alone is not as predictive as using lexical or acoustic features. However, adding them to lexical or acoustic feature set yields improvement compared to using lexical or acoustic features alone. This indicates that disfluencies and non-verbal vocalisations provide useful information overlooked by the other two types of features for emotion recognition.
Authors: Leimin Tian, Catherine Lai, Johanna D. Moore
Institution: University of Edinburgh
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2787934880