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 1976–2000 of 2041 papers

TitleStatusHype
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization—0
結合非線性動態特徵之語音情緒辨識(Speech Emotion Recognition via Nonlinear Dynamical Features)[In Chinese]—0
Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification—0
Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features—0
DeXpression: Deep Convolutional Neural Network for Expression RecognitionCode0
JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis—0
Recognition of Emotions using Kinects—0
Ensemble of Hankel Matrices for Face Emotion Recognition—0
A Linked Data Model for Multimodal Sentiment and Emotion Analysis—0
Topic-Based Chinese Message Sentiment Analysis: A Multilayered Analysis System—0
Emotion in Code-switching Texts: Corpus Construction and Analysis—0
Emotion Detection in Code-switching Texts via Bilingual and Sentimental Information—0
Emotion Analysis of Songs Based on Lyrical and Audio Features—0
Using Hankel Matrices for Dynamics-based Facial Emotion Recognition and Pain Detection—0
Tools for Digital Humanities: Enabling Access to the Old Occitan Romance of Flamenca—0
3D Model-Based Continuous Emotion Recognition—0
A Real Time Facial Expression Classification System Using Local Binary Patterns—0
Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks—0
MixedEmotions: Social Semantic Emotion Analysis for Innovative Multilingual Big Data Analytics Markets—0
Sentiment analysis on conversational texts—0
EmoNets: Multimodal deep learning approaches for emotion recognition in video—0
Affective Music Information Retrieval—0
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Training Deep Neural Networks on Noisy Labels with BootstrappingCode1
Discriminating Neutral and Emotional Speech using Neural Networks—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