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 1401–1425 of 2041 papers

TitleStatusHype
Improving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations—0
Improving Emotion Recognition Accuracy with Personalized Clustering—0
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations—0
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression—0
Improving Facial Emotion Recognition Systems Using Gradient and Laplacian Images—0
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation—0
Improving Language Models for Emotion Analysis: Insights from Cognitive Science—0
Improving Multimodal Accuracy Through Modality Pre-training and Attention—0
Improving Personalisation in Valence and Arousal Prediction using Data Augmentation—0
Improving Sentiment Analysis with Biofeedback Data—0
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation—0
Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs—0
Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms—0
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning—0
Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features—0
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement—0
Improving Unimodal Inference with Multimodal Transformers—0
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning—0
Inceptive Transformers: Enhancing Contextual Representations through Multi-Scale Feature Learning Across Domains and Languages—0
Inclusive Design Insights from a Preliminary Image-Based Conversational Search Systems Evaluation—0
Inconsistency-Aware Cross-Attention for Audio-Visual Fusion in Dimensional Emotion Recognition—0
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis—0
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training—0
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation—0
Emotion recognition in the times of COVID19: Coping with face masks—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