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 1676–1700 of 2041 papers

TitleStatusHype
Deep Learning based Emotion Recognition System Using Speech Features and TranscriptionsCode0
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild—0
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in TextsCode1
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN—0
KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation—0
ParallelDots at SemEval-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion Analysis—0
Facial Emotion Distribution Learning by Exploiting Low-Rank Label Correlations Locally—0
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English—0
A New Multilabel System for Automatic Music Emotion Recognition—0
Two-level Explanations in Music Emotion Recognition—0
EEG Classification by factoring in Sensor Configuration—0
Oculum afficit: Ocular Affect Recognition—0
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis—0
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis—0
A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition—0
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances—0
Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal NetworksCode0
Group Emotion Recognition Using Machine Learning—0
A new model for the implementation of positive and negative emotion recognition—0
Automatic Emotion Recognition (AER) System based on Two-Level Ensemble of Lightweight Deep CNN Models—0
Human-Centered Emotion Recognition in Animated GIFsCode0
A Self-Attentive Emotion Recognition NetworkCode0
Speech Emotion Recognition Using Multi-hop Attention MechanismCode0
A Personalized Affective Memory Neural Model for Improving Emotion Recognition—0
An Interaction-aware Attention Network for Speech Emotion Recognition in Spoken DialogsCode0
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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