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 451–475 of 2041 papers

TitleStatusHype
Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion RecognitionCode0
CARER: Contextualized Affect Representations for Emotion RecognitionCode0
ExpNet: Landmark-Free, Deep, 3D Facial ExpressionsCode0
Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion AttributionCode0
Exploiting Pseudo Future Contexts for Emotion Recognition in ConversationsCode0
An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition PerformanceCode0
Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask LearningCode0
Exploiting Multiple EEG Data Domains with Adversarial LearningCode0
Facial Emotion Recognition: A multi-task approach using deep learningCode0
Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation NetworkCode0
Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric LearningCode0
An experimental study in Real-time Facial Emotion Recognition on new 3RL datasetCode0
Evaluation Metrics for Automated Typographic Poster GenerationCode0
ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion DatasetsCode0
BYEL : Bootstrap Your Emotion LatentCode0
Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised ModelsCode0
Cross Lingual Speech Emotion Recognition: Urdu vs. Western LanguagesCode0
Crossmodal ASR Error Correction with Discrete Speech UnitsCode0
Multimodal Utterance-level Affect Analysis using Visual, Audio and Text FeaturesCode0
Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological SignalsCode0
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from SpeechCode0
Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic FeaturesCode0
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The BenchmarkCode0
Building a Dialogue Corpus Annotated with Expressed and Experienced EmotionsCode0
BSC-UPC at EmoSPeech-IberLEF2024: Attention Pooling for Emotion RecognitionCode0
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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