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 1501–1550 of 2041 papers

TitleStatusHype
Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis—0
Lie-Sensor: A Live Emotion Verifier or a Licensor for Chat Applications using Emotional Intelligence—0
LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks—0
Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study—0
Linguistic Linked Data for Sentiment Analysis—0
LIPN-UAM at EmoInt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination—0
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks—0
LLDif: Diffusion Models for Low-light Emotion Recognition—0
LLM supervised Pre-training for Multimodal Emotion Recognition in Conversations—0
LMR-CBT: Learning Modality-fused Representations with CB-Transformer for Multimodal Emotion Recognition from Unaligned Multimodal Sequences—0
Locally temporal-spatial pattern learning with graph attention mechanism for EEG-based emotion recognition—0
Low Rank Fusion based Transformers for Multimodal Sequences—0
LRDif: Diffusion Models for Under-Display Camera Emotion Recognition—0
LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets—0
Lyrics, Music, and Emotions—0
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP—0
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation—0
M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation—0
M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues—0
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods—0
Manipulating emotions for ground truth emotion analysis—0
Mapping the Technological Future: A Topic, Sentiment, and Emotion Analysis in Social Media Discourse—0
MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation—0
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation—0
MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting—0
MASR: Multi-label Aware Speech Representation—0
MATER: Multi-level Acoustic and Textual Emotion Representation for Interpretable Speech Emotion Recognition—0
MDAN: Multi-level Dependent Attention Network for Visual Emotion Analysis—0
M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition—0
MDEAW: A Multimodal Dataset for Emotion Analysis through EDA and PPG signals from wireless wearable low-cost off-the-shelf Devices—0
Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey—0
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations—0
MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis—0
MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition—0
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes—0
MERGE -- A Bimodal Audio-Lyrics Dataset for Static Music Emotion Recognition—0
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning—0
Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning—0
Meta Transfer Learning for Emotion Recognition—0
Meta Transfer Learning for Facial Emotion Recognition—0
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction—0
MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition—0
MFCC based Enlargement of the Training Set for Emotion Recognition in Speech—0
MFHCA: Enhancing Speech Emotion Recognition Via Multi-Spatial Fusion and Hierarchical Cooperative Attention—0
MicroEmo: Time-Sensitive Multimodal Emotion Recognition with Micro-Expression Dynamics in Video Dialogues—0
Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm—0
MIKU-PAL: An Automated and Standardized Multi-Modal Method for Speech Paralinguistic and Affect Labeling—0
Mining Call Center Conversations Exhibiting Similar Affective States—0
Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition—0
Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design—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