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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 951–1000 of 2041 papers

TitleStatusHype
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech—0
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech RepresentationsCode1
Emotion Detection From Tweets Using a BERT and SVM Ensemble ModelCode0
A Novel Enhanced Convolution Neural Network with Extreme Learning Machine: Facial Emotional Recognition in Psychology Practices—0
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 ChallengeCode0
Extending RNN-T-based speech recognition systems with emotion and language classification—0
Contextual Information and Commonsense Based Prompt for Emotion Recognition in ConversationCode1
A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition—0
Multimodal Speech Emotion Recognition using Cross Attention with Aligned Audio and Text—0
GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion DetectionCode1
Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-DistributionsCode0
Seeking Subjectivity in Visual Emotion Distribution Learning—0
Affective Behaviour Analysis Using Pretrained Model with Facial PrioriCode1
Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive LossCode1
Affective Behavior Analysis using Action Unit Relation Graph and Multi-task Cross Attention—0
BYEL : Bootstrap Your Emotion LatentCode0
HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images—0
Multi-Task Learning Framework for Emotion Recognition in-the-wildCode0
CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion RecognitionCode0
A Multibias-mitigated and Sentiment Knowledge Enriched Transformer for Debiasing in Multimodal Conversational Emotion Recognition—0
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
Semi-supervised cross-lingual speech emotion recognition—0
MDEAW: A Multimodal Dataset for Emotion Analysis through EDA and PPG signals from wireless wearable low-cost off-the-shelf Devices—0
Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion RecognitionCode1
EmoSens: Emotion Recognition based on Sensor data analysis using LightGBM—0
Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource DevicesCode0
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion RecognitionCode0
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition—0
GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion RecognitionCode1
A cross-corpus study on speech emotion recognition—0
Classifying emotions and engagement in online learning based on a single facial expression recognition neural network—0
A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition—0
Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets—0
CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation—0
Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data—0
SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning—0
Emotion recognition in the times of COVID19: Coping with face masks—0
Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers—0
The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and StressCode1
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion RecognitionCode0
The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in ConversationCode1
HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence—0
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition—0
AHD ConvNet for Speech Emotion Classification—0
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets—0
Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction—0
AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition—0
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation—0
A Multimodal Corpus for Emotion Recognition in SarcasmCode1
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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