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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 1851–1875 of 2041 papers

TitleStatusHype
Domain Adversarial for Acoustic Emotion Recognition—0
Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep LearningCode0
On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks—0
Reusing Neural Speech Representations for Auditory Emotion Recognition—0
EEG emotion recognition using dynamical graph convolutional neural networks—0
Speech Emotion Recognition Considering Local Dynamic Features—0
Deep learning for affective computing: text-based emotion recognition in decision support—0
Cross-lingual and Multilingual Speech Emotion Recognition on English and French—0
Deep factorization for speech signal—0
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets—0
Deep Multimodal Learning for Emotion Recognition in Spoken Language—0
Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition—0
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation—0
Multi-attention Recurrent Network for Human Communication ComprehensionCode0
ExpNet: Landmark-Free, Deep, 3D Facial ExpressionsCode0
HoloFace: Augmenting Human-to-Human Interactions on HoloLensCode0
Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks—0
Transfer Learning for Improving Speech Emotion Classification AccuracyCode0
Facial emotion recognition using min-max similarity classifier—0
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study—0
Learning Spontaneity to Improve Emotion Recognition In Speech—0
A Novel Trajectory-based Spatial-Temporal Spectral Features for Speech Emotion Recognition—0
Forewords—0
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training—0
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video—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