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 1601–1625 of 2041 papers

TitleStatusHype
Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model—0
Bimodal Speech Emotion Recognition Using Pre-Trained Language Models—0
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis—0
A Time Series Analysis of Emotional Loading in Central Bank Statements—0
Modeling emotion in complex stories: the Stanford Emotional Narratives DatasetCode0
MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion RecognitionCode0
Emotion Recognition for Vietnamese Social Media Text—0
Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping—0
Real-Time Emotion Recognition via Attention Gated Hierarchical Memory NetworkCode0
Joint Emotion Label Space Modelling for Affect Lexica—0
Speech Emotion Recognition Using Speech Feature and Word EmbeddingCode0
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis—0
Adaptive Fusion Techniques for Multimodal Data—0
M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues—0
Addressing Ambiguity of Emotion Labels Through Meta-Learning—0
Speaker-invariant Affective Representation Learning via Adversarial Training—0
An Affective Situation Labeling System from Psychological Behaviors in Emotion Recognition—0
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis—0
Modeling Feature Representations for Affective Speech using Generative Adversarial Networks—0
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition—0
DENS: A Dataset for Multi-class Emotion Analysis—0
Domain adversarial learning for emotion recognition—0
AI in Pursuit of Happiness, Finding Only Sadness: Multi-Modal Facial Emotion Recognition Challenge—0
Emotion recognition with 4kresolution database—0
Conversational Emotion Analysis via Attention Mechanisms—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