Abstract
Human conversation analysis is challenging because the meaning can be expressed through words, intonation, or even body language and facial expression. We introduce a hierarchical encoder-decoder structure with attention mechanism for conversation analysis. The hierarchical encoder learns word-level features from video, audio, and text data that are then formulated into conversation-level features. The corresponding hierarchical decoder is able to predict different attributes at given time instances. To integrate multiple sensory inputs, we introduce a novel fusion strategy with modality attention. We evaluated our system on published emotion recognition, sentiment analysis, and speaker trait analysis datasets. Our system outperformed previous state-of-the-art approaches in both classification and regressions tasks on three datasets. We also outperformed previous approaches in generalization tests on two commonly used datasets. We achieved comparable performance in predicting co-existing labels using the proposed model instead of multiple individual models. In addition, the easily-visualized modality and temporal attention demonstrated that the proposed attention mechanism helps feature selection and improves model interpretability.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Proceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.