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 851–875 of 2041 papers

TitleStatusHype
Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors—0
Beyond Classification: Towards Speech Emotion Reasoning with Multitask AudioLLMs—0
Dynamic Resolution Guidance for Facial Expression Recognition—0
Ethics Sheets for AI Tasks—0
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals—0
An Attribute-Aligned Strategy for Learning Speech Representation—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognition—0
3D Human Sensing, Action and Emotion Recognition in Robot Assisted Therapy of Children With Autism—0
Better Spanish Emotion Recognition In-the-wild: Bringing Attention to Deep Spectrum Voice Analysis—0
Dynamic Model of Facial Expression Recognition based on Eigen-face Approach—0
Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets—0
Evaluation of OpenAI o1: Opportunities and Challenges of AGI—0
Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition—0
Evaluation of Speaker Anonymization on Emotional Speech—0
Event Based Emotion Classification for News Articles—0
EventFormer: AU Event Transformer for Facial Action Unit Event Detection—0
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition—0
EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation—0
Conversational Emotion Analysis via Attention Mechanisms—0
Adversarial Auto-encoders for Speech Based Emotion Recognition—0
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos—0
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities—0
Exploiting Facial Landmarks for Emotion Recognition in the Wild—0
Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training—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