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 601–625 of 2041 papers

TitleStatusHype
EmoTxt: A Toolkit for Emotion Recognition from TextCode0
A Multi-task Neural Approach for Emotion Attribution, Classification and SummarizationCode0
Audio Explanation Synthesis with Generative Foundation ModelsCode0
Emotion Action Detection and Emotion Inference: the Task and DatasetCode0
EmojiHeroVR: A Study on Facial Expression Recognition under Partial Occlusion from Head-Mounted DisplaysCode0
Emotional Speech Recognition with Pre-trained Deep Visual ModelsCode0
Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk Differentiation in Chinese Psychological Support HotlinesCode0
Deep Learning based Emotion Recognition System Using Speech Features and TranscriptionsCode0
Efficient Low-rank Multimodal Fusion with Modality-Specific FactorsCode0
Efficient Arabic emotion recognition using deep neural networksCode0
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future DirectionsCode0
Infant Cry Emotion Recognition Using Improved ECAPA-TDNN with Multiscale Feature Fusion and Attention EnhancementCode0
Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation NetworkCode0
Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data ScenariosCode0
Deep Evolution for Facial Emotion Recognition—0
AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition—0
Attributes-aware Visual Emotion Representation Learning—0
A Multimodal Emotion Recognition System: Integrating Facial Expressions, Body Movement, Speech, and Spoken Language—0
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly—0
Deep CNN with late fusion for realtime multimodal emotion recognition—0
Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition—0
Addressing Racial Bias in Facial Emotion Recognition—0
Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks—0
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition—0
Decoding Emotional Experience through Physiological Signal Processing—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