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

Showing 12761300 of 2041 papers

TitleStatusHype
Two-level Attention with Two-stage Multi-task Learning for Facial Emotion Recognition0
Two-level Explanations in Music Emotion Recognition0
Two-stage Framework for Robust Speech Emotion Recognition Using Target Speaker Extraction in Human Speech Noise Conditions0
UMUTeam@LT-EDI-ACL2022: Detecting Signs of Depression from text0
UMUTeam@TamilNLP-ACL2022: Emotional Analysis in Tamil0
Uncertainty Estimation in the Real World: A Study on Music Emotion Recognition0
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories0
Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data0
Unifying Categorical Models by Explicit Disentanglement of the Labels' Generative Factors0
Unifying EEG and Speech for Emotion Recognition: A Two-Step Joint Learning Framework for Handling Missing EEG Data During Inference0
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis0
Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition0
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause0
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion0
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion0
Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System0
Unsupervised Cross-Lingual Speech Emotion Recognition Using DomainAdversarial Neural Network0
Unsupervised Discovery of Facial Landmarks and Head Pose0
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition0
Unsupervised low-rank representations for speech emotion recognition0
Unsupervised Multimodal Language Representations using Convolutional Autoencoders0
Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data0
Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech0
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition0
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Benchmark Results

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