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
Conversational Transfer Learning for Emotion Recognition—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
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances—0
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition—0
Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels—0
A Novel Trajectory-based Spatial-Temporal Spectral Features for Speech Emotion Recognition—0
AHD ConvNet for Speech Emotion Classification—0
Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units—0
Emotion Recognition in Context—0
Facial Expression Recognition using Squeeze and Excitation-powered Swin Transformers—0
Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis—0
Combining Heterogeneous User Generated Data to Sense Well-being—0
Emotion Recognition from the perspective of Activity Recognition—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—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
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition—0
Emotion Recognition from Speech based on Relevant Feature and Majority Voting—0
COIN: Conversational Interactive Networks for Emotion Recognition in Conversation—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