Emotion Recognition
467 papers with code • 7 benchmarks • 45 datasets
Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition
Benchmarks
These leaderboards are used to track progress in Emotion Recognition
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Libraries
Use these libraries to find Emotion Recognition models and implementationsDatasets
Hateful Memes
FER2013
Aff-Wild
CARER
SEED
MSP-IMPROV
Aff-Wild2
ISEAR
Hateful Memes Challenge
EmotionLines
Subtasks
Latest papers with no code
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences
In this paper, we propose a contrastive learning framework utilizing selective strong augmentation (SSA) for self-supervised gait-based emotion representation, which aims to derive effective representations from limited labeled gait data.
Empathy Through Multimodality in Conversational Interfaces
Agents represent one of the most emerging applications of Large Language Models (LLMs) and Generative AI, with their effectiveness hinging on multimodal capabilities to navigate complex user environments.
Adapting WavLM for Speech Emotion Recognition
Recently, the usage of speech self-supervised models (SSL) for downstream tasks has been drawing a lot of attention.
ESIHGNN: Event-State Interactions Infused Heterogeneous Graph Neural Network for Conversational Emotion Recognition
Toward this end, we propose a novel graph-based approach, namely Event-State Interactions infused Heterogeneous Graph Neural Network (ESIHGNN), which incorporates the speaker's emotional state and constructs a heterogeneous event-state interaction graph to model the conversation.
GMP-ATL: Gender-augmented Multi-scale Pseudo-label Enhanced Adaptive Transfer Learning for Speech Emotion Recognition via HuBERT
The continuous evolution of pre-trained speech models has greatly advanced Speech Emotion Recognition (SER).
Toward end-to-end interpretable convolutional neural networks for waveform signals
This paper introduces a novel convolutional neural networks (CNN) framework tailored for end-to-end audio deep learning models, presenting advancements in efficiency and explainability.
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model
A major challenge of expressive VC lies in emotion prosody modeling.
EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model
In contrast, long sequential videos can reveal authentic emotions; 2) Previous studies commonly utilize various signals such as facial, speech, and even sensitive biological signals (e. g., electrocardiogram).
Usefulness of Emotional Prosody in Neural Machine Translation
In this work, we propose to improve translation quality by adding another external source of information: the automatically recognized emotion in the voice.
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
Since consistency and complementarity information correspond to low-frequency and high-frequency information, respectively, this paper revisits the problem of multimodal emotion recognition in conversation from the perspective of the graph spectrum.
