SOTAVerified

Emotion Recognition

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

Papers

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Showing 826–850 of 2041 papers

TitleStatusHype
Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion RecognitionCode0
Multi-label Emotion Analysis in Conversation via Multimodal Knowledge Distillation—0
Affective Video Content Analysis: Decade Review and New Perspectives—0
Privacy-preserving Representation Learning for Speech Understanding—0
Data Augmentation for Emotion Detection in Small Imbalanced Text DataCode0
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning—0
Do Stochastic Parrots have Feelings Too? Improving Neural Detection of Synthetic Text via Emotion RecognitionCode0
The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 LanguagesCode0
Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition—0
Customising General Large Language Models for Specialised Emotion Recognition Tasks—0
MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data—0
Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition—0
Towards Subject Agnostic Affective Emotion Recognition—0
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel AttentionCode0
EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks—0
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed DialoguesCode0
Bias in Emotion Recognition with ChatGPT—0
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference—0
Towards Emotion-Based Synthetic Consciousness: Using LLMs to Estimate Emotion Probability Vectors—0
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation—0
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations—0
Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning—0
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation—0
Prompting Audios Using Acoustic Properties For Emotion Representation—0
Graph Neural Network-based EEG Classification: A Survey—0
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All datasetsEmomusicRAVDESSEMOTICFER2013MaSaC_ERCMPEDMSP-PodcastSEEDคลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1M2D-CLAPEmoA77.4—Unverified
2M2D2EmoA76.7—Unverified
3M2DEmoA76.1—Unverified
4Jukebox (Pre-training: CALM)EmoA72.1—Unverified
5CLMR (Pre-training: contrastive)EmoA67.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+AttentionAccuracy86.7—Unverified
2MultiMAE-DERWAR83.61—Unverified
3Intermediate-Attention-FusionAccuracy81.58—Unverified
4Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedSTAccuracy80.08—Unverified
5ERANN-0-4Accuracy74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CAGETop-3 Accuracy (%)14.73—Unverified
2FocusCLIPTop-3 Accuracy (%)13.73—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy66.13—Unverified
#ModelMetricClaimedVerifiedStatus
1MaSaC-ERC-ZF1-score (Weighted)51.17—Unverified
#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy40.34—Unverified
#ModelMetricClaimedVerifiedStatus
1w2v2-L-robust-12Concordance correlation coefficient (CCC)0.64—Unverified
#ModelMetricClaimedVerifiedStatus
14D-aNNAccuracy96.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN1'"1—Unverified