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
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities—0
Expression Recognition Analysis in the Wild—0
Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents—0
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer—0
Extending RNN-T-based speech recognition systems with emotion and language classification—0
Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network—0
Face Detection on Mobile: Five Implementations and Analysis—0
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition—0
Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models—0
Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units—0
Facial Emotion Distribution Learning by Exploiting Low-Rank Label Correlations Locally—0
Facial Expression Recognition using Squeeze and Excitation-powered Swin Transformers—0
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation—0
Facial Emotion Recognition in VR Games—0
Adaptive Fusion Techniques for Multimodal Data—0
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages—0
Facial Emotion Recognition using Convolutional Neural Networks—0
Facial Emotion Recognition using CNN in PyTorch—0
Facial Emotion Recognition Using Deep Learning—0
Facial Emotion Recognition using Deep Residual Networks in Real-World Environments—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets—0
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection—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