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
EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification MethodologiesCode1
Multi-Label Compound Expression Recognition: C-EXPR Database & Network—0
Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in ConversationCode1
Feature Selection Approaches for Optimising Music Emotion Recognition Methods—0
Quality at the Tail of Machine Learning Inference—0
A speech corpus of Quechua Collao for automatic dimensional emotion recognitionCode0
AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition—0
Large Raw Emotional Dataset with Aggregation MechanismCode1
Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals—0
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts TasksCode0
Multimodal Emotion Recognition among Couples from Lab Settings to Daily Life using Smartwatches—0
InterMulti:Multi-view Multimodal Interactions with Text-dominated Hierarchical High-order Fusion for Emotion Analysis—0
Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features—0
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods—0
EffMulti: Efficiently Modeling Complex Multimodal Interactions for Emotion Analysis—0
Disentangling Prosody Representations with Unsupervised Speech Reconstruction—0
An Approach for Improving Automatic Mouth Emotion Recognition—0
A comparative study of emotion recognition methods using facial expressions—0
A survey of smart classroom: Concept, technologies and facial emotions recognition application—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
Analysis of constant-Q filterbank based representations for speech emotion recognition—0
Whose Emotion Matters? Speaking Activity Localisation without Prior KnowledgeCode0
NLP meets psychotherapy: Using predicted client emotions and self-reported client emotions to measure emotional coherence—0
STILN: A Novel Spatial-Temporal Information Learning Network for EEG-based Emotion Recognition—0
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion RecognitionCode2
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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