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 826–850 of 2041 papers

TitleStatusHype
Using Auxiliary Tasks In Multimodal Fusion Of Wav2vec 2.0 And BERT For Multimodal Emotion Recognition—0
A low latency attention module for streaming self-supervised speech representation learningCode0
Multi-Modality in Music: Predicting Emotion in Music from High-Level Audio Features and LyricsCode0
Partial Label Learning for Emotion Recognition from EEGCode1
Ensemble knowledge distillation of self-supervised speech models—0
ChatGPT: Jack of all trades, master of noneCode1
Knowledge-aware Bayesian Co-attention for Multimodal Emotion Recognition—0
Medical Face Masks and Emotion Recognition from the Body: Insights from a Deep Learning PerspectiveCode0
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition—0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Graph-Enhanced Emotion Neural DecodingCode0
NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion AnalysisCode0
A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination—0
Cluster-Level Contrastive Learning for Emotion Recognition in ConversationsCode1
Audio Representation Learning by Distilling Video as Privileged Information—0
cross-modal fusion techniques for utterance-level emotion recognition from text and speech—0
deep learning of segment-level feature representation for speech emotion recognition in conversations—0
CSAT‑FTCN: A Fuzzy‑Oriented Model with Contextual Self‑attention Network for Multimodal Emotion Recognition—0
Facial Expression Recognition using Squeeze and Excitation-powered Swin Transformers—0
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation—0
Modulation spectral features for speech emotion recognition using deep neural networks—0
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks—0
Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN—0
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring—0
A Novel Exploitative and Explorative GWO-SVM Algorithm for Smart Emotion Recognition—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