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 801–825 of 2041 papers

TitleStatusHype
Multimodal Group Emotion Recognition In-the-wild Using Privacy-Compliant Features—0
Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data—0
LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks—0
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation TechniqueCode0
Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data—0
ESTformer: Transformer Utilizing Spatiotemporal Dependencies for Electroencaphalogram Super-resolution—0
Optimal EEG Electrode Set for Emotion Recognition From Brain Signals: An Empirical Quest—0
SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults—0
Dialogue Quality and Emotion Annotations for Customer Support ConversationsCode0
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition—0
Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces—0
Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification TasksCode0
Implementation of AI Deep Learning Algorithm For Multi-Modal Sentiment Analysis—0
Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots—0
Enhancing Student Engagement in Online Learning through Facial Expression Analysis and Complex Emotion Recognition using Deep Learning—0
Accommodating Missing Modalities in Time-Continuous Multimodal Emotion Recognition—0
Improving Unimodal Inference with Multimodal Transformers—0
On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition—0
Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach—0
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning—0
Accumulating Word Representations in Multi-level Context Integration for ERC TaskCode0
Multimodal Stress Detection Using Facial Landmarks and Biometric Signals—0
New Approach for an Affective Computing-Driven Quality of Experience (QoE) Prediction—0
An analysis of large speech models-based representations for speech emotion recognition—0
Emotional Theory of Mind: Bridging Fast Visual Processing with Slow Linguistic Reasoning—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