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 1651–1675 of 2041 papers

TitleStatusHype
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning—0
EmotionX-IDEA: Emotion BERT -- an Affectional Model for ConversationCode0
Context-Aware Emotion Recognition NetworksCode0
Multimodal Emotion Recognition Using Deep Canonical Correlation AnalysisCode0
User independent Emotion Recognition with Residual Signal-Image Network—0
Emotionless: Privacy-Preserving Speech Analysis for Voice AssistantsCode1
Pitch-Synchronous Single Frequency Filtering Spectrogram for Speech Emotion Recognition—0
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning—0
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets—0
EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings—0
Deep Temporal Analysis for Non-Acted Body Affect Recognition—0
EEG-Based Emotion Recognition Using Regularized Graph Neural NetworksCode1
Comparison of Classical Machine Learning Approaches on Bangla Textual Emotion AnalysisCode0
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition—0
Towards Explainable Music Emotion Recognition: The Route via Mid-level Features—0
Attending to Emotional NarrativesCode0
Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition—0
EmotionX-KU: BERT-Max based Contextual Emotion ClassifierCode0
End-to-End Emotional Speech Synthesis Using Style Tokens and Semi-Supervised Training—0
Emotion Recognition Using Fusion of Audio and Video Features—0
Multimodal and Multi-view Models for Emotion Recognition—0
Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition—0
Identifying Emotions from Walking using Affective and Deep Features—0
Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation—0
Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition—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