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 951–975 of 2041 papers

TitleStatusHype
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection—0
Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation—0
Crowdsourcing a Word-Emotion Association Lexicon—0
FindingEmo: An Image Dataset for Emotion Recognition in the Wild—0
Finding Good Representations of Emotions for Text Classification—0
Findings of the Shared Task on Emotion Analysis in Tamil—0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
Fine-Grained Emotion Detection in Health-Related Online Posts—0
Fine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation—0
CSAT‑FTCN: A Fuzzy‑Oriented Model with Contextual Self‑attention Network for Multimodal Emotion Recognition—0
Fine-tuning Wav2vec for Vocal-burst Emotion Recognition—0
Fitting Different Interactive Information: Joint Classification of Emotion and Intention—0
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition—0
Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition—0
Human Pose Descriptions and Subject-Focused Attention for Improved Zero-Shot Transfer in Human-Centric Classification Tasks—0
CUET-NLP@TamilNLP-ACL2022: Multi-Class Textual Emotion Detection from Social Media using Transformer—0
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation—0
Fractal Dimension Pattern Based Multiresolution Analysis for Rough Estimator of Person-Dependent Audio Emotion Recognition—0
An Architecture for Accelerated Large-Scale Inference of Transformer-Based Language Models—0
General Purpose Textual Sentiment Analysis and Emotion Detection Tools—0
Technical Approach for the EMI Challenge in the 8th Affective Behavior Analysis in-the-Wild Competition—0
Framewise approach in multimodal emotion recognition in OMG challenge—0
An Approach for Improving Automatic Mouth Emotion Recognition—0
Improving Sentiment Analysis with Biofeedback Data—0
Dual Prototyping with Domain and Class Prototypes for Affective Brain-Computer Interface in Unseen Target Conditions—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