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 1926–1950 of 2041 papers

TitleStatusHype
Sample Correlation for Fingerprinting Deep Face RecognitionCode0
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts TasksCode0
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 ChallengeCode0
Transformer for Emotion RecognitionCode0
Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the WildCode0
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation TechniqueCode0
DiTMoS: Delving into Diverse Tiny-Model Selection on MicrocontrollersCode0
A Compact Embedding for Facial Expression SimilarityCode0
CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversationsCode0
Facial Emotion Recognition: A multi-task approach using deep learningCode0
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment AnalysisCode0
ExpNet: Landmark-Free, Deep, 3D Facial ExpressionsCode0
TGCA-PVT: Topic-Guided Context-Aware Pyramid Vision Transformer for Sticker Emotion RecognitionCode0
NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion AnalysisCode0
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion AnalysisCode0
Linking emotions to behaviors through deep transfer learningCode0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
Listen and Speak Fairly: A Study on Semantic Gender Bias in Speech Integrated Large Language ModelsCode0
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic FeaturesCode0
Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion TechniquesCode0
Comparison of Classical Machine Learning Approaches on Bangla Textual Emotion AnalysisCode0
Audio-Linguistic Embeddings for Spoken SentencesCode0
Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource DevicesCode0
Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in ConversationCode0
A low latency attention module for streaming self-supervised speech representation learningCode0
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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