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 1776–1800 of 2041 papers

TitleStatusHype
CUET-NLP@TamilNLP-ACL2022: Multi-Class Textual Emotion Detection from Social Media using Transformer—0
CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding—0
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels—0
Customising General Large Language Models for Specialised Emotion Recognition Tasks—0
DAGAM: A Domain Adversarial Graph Attention Model for Subject Independent EEG-Based Emotion Recognition—0
DASB -- Discrete Audio and Speech Benchmark—0
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Data Fine-tuning—0
DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks—0
Deciphering Emotions in Children Storybooks: A Comparative Analysis of Multimodal LLMs in Educational Applications—0
Decoding Emotional Experience through Physiological Signal Processing—0
Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks—0
Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition—0
Deep CNN with late fusion for realtime multimodal emotion recognition—0
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly—0
Deep Evolution for Facial Emotion Recognition—0
Deep factorization for speech signal—0
Deep Factorization for Speech Signal—0
Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition—0
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations—0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Deep Learning as Feature Encoding for Emotion Recognition—0
Deep learning for affective computing: text-based emotion recognition in decision support—0
Deep Learning for Speech Emotion Recognition: A CNN Approach Utilizing Mel Spectrograms—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