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 1551–1575 of 2041 papers

TitleStatusHype
Mitigating Group Bias in Federated Learning for Heterogeneous Devices—0
Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach—0
MixedEmotions: Social Semantic Emotion Analysis for Innovative Multilingual Big Data Analytics Markets—0
MMDS: A Multimodal Medical Diagnosis System Integrating Image Analysis and Knowledge-based Departmental Consultation—0
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation—0
MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data—0
Modality-based Factorization for Multimodal Fusion—0
Modality Influence in Multimodal Machine Learning—0
Modeling Feature Representations for Affective Speech using Generative Adversarial Networks—0
Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions—0
Modelling Emotion Dynamics in Song Lyrics with State Space Models—0
Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics—0
Modelling Representation Noise in Emotion Analysis using Gaussian Processes—0
Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos—0
Modulation spectral features for speech emotion recognition using deep neural networks—0
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition—0
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition—0
MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition—0
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling—0
MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition—0
MUCS@DravidianLangTech@ACL2022: Ensemble of Logistic Regression Penalties to Identify Emotions in Tamil Text—0
Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition—0
Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems—0
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition—0
Multi-Cue Adaptive Emotion Recognition Network—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