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 1501–1525 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
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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