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 1801–1825 of 2041 papers

TitleStatusHype
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period—0
deep learning of segment-level feature representation for speech emotion recognition in conversations—0
Deep Metric Structured Learning For Facial Expression Recognition—0
Deep Multimodal Learning for Emotion Recognition in Spoken Language—0
Deep Net Features for Complex Emotion Recognition—0
Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data—0
Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
Deep Residual Local Feature Learning for Speech Emotion Recognition—0
Deep scattering network for speech emotion recognition—0
Deep-seeded Clustering for Emotion Recognition from Wearable Physiological Sensors—0
Deep Temporal Analysis for Non-Acted Body Affect Recognition—0
Demonstration of IlluMe: Creating Ambient According to Instant Message Logs—0
DENS: A Dataset for Multi-class Emotion Analysis—0
Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News—0
DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News—0
Depression detection from Social Media Bangla Text Using Recurrent Neural Networks—0
Depression detection in social media posts using affective and social norm features—0
DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition—0
Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment—0
Describing emotions with acoustic property prompts for speech emotion recognition—0
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP—0
Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition—0
Detecting Emotion Carriers by Combining Acoustic and Lexical Representations—0
Developing a High-performance Framework for Speech Emotion Recognition in Naturalistic Conditions Challenge for Emotional Attribute Prediction—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