ArticleSensors (Basel, Switzerland)2021
Robust Multimodal Emotion Recognition from Conversation with Transformer-Based Crossmodality Fusion.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Empowering emotional intelligence through deep learning techniques.Scientific reports · 2025Article
- Spatiotemporal multimodal emotion recognition using Temporal video sequences and pose features for child emotion classification.Scientific reports · 2025Article
- A Comprehensive Review of Multimodal Emotion Recognition: Techniques, Challenges, and Future Directions.Biomimetics (Basel, Switzerland) · 2025Review
- Emotion Forecasting: A Transformer-Based Approach.Journal of medical Internet research · 2025Article
- Analysis of the fusion of multimodal sentiment perception and physiological signals in Chinese-English cross-cultural communication: Transformer approach incorporating self-attention enhancement.PeerJ. Computer science · 2025Article
- Speech Emotion Recognition Using Convolution Neural Networks and Multi-Head Convolutional Transformer.Sensors (Basel, Switzerland) · 2023Article
- An Assessment of In-the-Wild Datasets for Multimodal Emotion Recognition.Sensors (Basel, Switzerland) · 2023Article
- GCFFrontiers in neuroscience · 2023Article
- Artificial intelligence in cardiology: the debate continues.European heart journal. Digital health · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
Decades of scientific research have been conducted on developing and evaluating methods for automated emotion recognition. With exponentially growing technology, there is a wide range of emerging applications that require emotional state recognition of the user. This paper investigates a robust approach for multimodal emotion recognition during a conversation. Three separate models for audio, video and text modalities are structured and fine-tuned on the MELD. In this paper, a transformer-based crossmodality fusion with the EmbraceNet architecture is employed to estimate the emotion. The proposed multimodal network architecture can achieve up to 65% accuracy, which significantly surpasses any of the unimodal models. We provide multiple evaluation techniques applied to our work to show that our model is robust and can even outperform the state-of-the-art models on the MELD.
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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.