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 901–925 of 2041 papers

TitleStatusHype
Leveraging Label Correlations in a Multi-label Setting: A Case Study in EmotionCode1
Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalitiesCode1
Multilevel Transformer For Multimodal Emotion Recognition—0
Effect of different splitting criteria on the performance of speech emotion recognition—0
Knowledge Transfer For On-Device Speech Emotion Recognition with Neural Structured LearningCode0
Pretrained audio neural networks for Speech emotion recognition in PortugueseCode0
Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context—0
Towards emotion recognition for virtual environments: an evaluation of EEG features on benchmark dataset—0
Speech Emotion Recognition via an Attentive Time-Frequency Neural Network—0
Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge SelectionCode1
PERI: Part Aware Emotion Recognition In The Wild—0
Modelling Emotion Dynamics in Song Lyrics with State Space Models—0
Supervised Prototypical Contrastive Learning for Emotion Recognition in ConversationCode1
Training speech emotion classifier without categorical annotations—0
Toward the application of XAI methods in EEG-based systems—0
Natural Language Processing for Cognitive Analysis of Emotions—0
In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus StudyCode1
Exploration of A Self-Supervised Speech Model: A Study on Emotional Corpora—0
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party ConversationsCode1
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis—0
基于关系图注意力网络和宽度学习的负面情绪识别方法(Negative Emotion Recognition Method Based on Rational Graph Attention Network and Broad Learning)—0
Using Extracted Emotion Cause to Improve Content-Relevance for Empathetic Conversation Generation—0
Fine-tuning Wav2vec for Vocal-burst Emotion Recognition—0
End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution LearningCode0
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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