Multimodal Emotion Recognition
39 papers with code • 3 benchmarks • 7 datasets
This is a leaderboard for multimodal emotion recognition on the IEMOCAP dataset. The modality abbreviations are A: Acoustic T: Text V: Visual
Please include the modality in the bracket after the model name.
All models must use standard five emotion categories and are evaluated in standard leave-one-session-out (LOSO). See the papers for references.
Benchmarks
These leaderboards are used to track progress in Multimodal Emotion Recognition
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Libraries
Use these libraries to find Multimodal Emotion Recognition models and implementations
IEMOCAP
MELD
CPED
EMOTIC
DEAP
Most implemented papers
Multimodal Speech Emotion Recognition and Ambiguity Resolution
Demfier/multimodal-speech-emotion-recognition
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In this work, we adopt a feature-engineering based approach to tackle the task of speech emotion recognition.
Multimodal Speech Emotion Recognition Using Audio and Text
david-yoon/multimodal-speech-emotion
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Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers.
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
Therefore, in this paper, based on audio and text, we consider the task of multimodal sentiment analysis and propose a novel fusion strategy including both multi-feature fusion and multi-modality fusion to improve the accuracy of audio-text sentiment analysis.
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
tzirakis/Multimodal-Emotion-Recognition
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The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.
Context-Dependent Sentiment Analysis in User-Generated Videos
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning
Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour.
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc.
Emotion Recognition in Audio and Video Using Deep Neural Networks
Humans are able to comprehend information from multiple domains for e. g. speech, text and visual.
Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling
Multimodal sentiment analysis is a very actively growing field of research.
Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition
We test our method in the EmotiW 2018 challenge and we gain promising results.
