Multimodal Emotion Recognition
48 papers with code • 3 benchmarks • 8 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.
Libraries
Use these libraries to find Multimodal Emotion Recognition models and implementationsMost implemented papers
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.
Music Mood Detection Based On Audio And Lyrics With Deep Neural Net
We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track.
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
Emotion recognition in conversations is crucial for building empathetic machines.
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis
We evaluate the performance of DCCA on five multimodal datasets: the SEED, SEED-IV, SEED-V, DEAP, and DREAMER datasets.
Learning Alignment for Multimodal Emotion Recognition from Speech
Further, emotion recognition will be beneficial from using audio-textual multimodal information, it is not trivial to build a system to learn from multimodality.
Multimodal Behavioral Markers Exploring Suicidal Intent in Social Media Videos
In this work, we set out to study multimodal behavioral markers related to suicidal intent when expressed on social media videos.
Attentive Modality Hopping Mechanism for Speech Emotion Recognition
In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system.
Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation
Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions.
Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition
Multimodal emotion recognition from speech is an important area in affective computing.