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 1476–1500 of 2041 papers

TitleStatusHype
Analysis of Resource-efficient Predictive Models for Natural Language Processing—0
Efficient Arabic emotion recognition using deep neural networksCode0
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition—0
Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation—0
Generative Adversarial Networks in Human Emotion Synthesis:A Review—0
Empirical Interpretation of Speech Emotion Perception with Attention Based Model for Speech Emotion Recognition—0
Speech-Based Emotion Recognition using Neural Networks and Information VisualizationCode0
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition—0
Hybrid Backpropagation Parallel Reservoir Networks—0
Emotion recognition by fusing time synchronous and time asynchronous representations—0
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
Deformable Convolutional LSTM for Human Body Emotion RecognitionCode0
Emotion controllable speech synthesis using emotion-unlabeled dataset with the assistance of cross-domain speech emotion recognition—0
Multi-stream Attention-based BLSTM with Feature Segmentation for Speech Emotion Recognition—0
A Multi-Componential Approach to Emotion Recognition and the Effect of Personality—0
Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training—0
Multi-Window Data Augmentation Approach for Speech Emotion Recognition—0
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation—0
A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures—0
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)—0
An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild—0
CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue—0
Embedded Emotions -- A Data Driven Approach to Learn Transferable Feature Representations from Raw Speech Input for Emotion Recognition—0
Deep Evolution for Facial Emotion Recognition—0
Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm—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