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 1751–1775 of 2041 papers

TitleStatusHype
Emotion Recognition From Speech With Recurrent Neural NetworksCode0
A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality ConversionCode0
Data Augmentation for Emotion Detection in Small Imbalanced Text DataCode0
VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion RecognitionCode0
Emotion Recognition from SpeechCode0
Multi-Task Learning Framework for Emotion Recognition in-the-wildCode0
The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 LanguagesCode0
Multi-attention Recurrent Network for Human Communication ComprehensionCode0
Pretext Tasks selection for multitask self-supervised speech representation learningCode0
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and BeyondCode0
Pretrained audio neural networks for Speech emotion recognition in PortugueseCode0
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural NetworkCode0
Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine LearningCode0
Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion RecognitionCode0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
Customising General Large Language Models for Specialised Emotion Recognition TasksCode0
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion AnalysisCode0
Multi-label Co-regularization for Semi-supervised Facial Action Unit RecognitionCode0
BSC-UPC at EmoSPeech-IberLEF2024: Attention Pooling for Emotion RecognitionCode0
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion RecognitionCode0
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal BehaviorsCode0
Bridging Modalities: Knowledge Distillation and Masked Training for Translating Multi-Modal Emotion Recognition to Uni-Modal, Speech-Only Emotion RecognitionCode0
Cultural-Aware AI Model for Emotion RecognitionCode0
Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion RecognitionCode0
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversationCode0
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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