ArticleProceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia2018
Human Conversation Analysis Using Attentive Multimodal Networks with Hierarchical Encoder-Decoder.
Article in Proceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A plug and play fuzzy mask extraction module for single image deraining.Scientific reports · 2025Article
- Mutual Correlation Attentive Factors in Dyadic Fusion Networks for Speech Emotion Recognition.Proceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia · 2019Article
- Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
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Authors and funding
8 authors.
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.