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 1776–1800 of 2041 papers

TitleStatusHype
Label-less Learning for Traffic Control in an Edge Network—0
EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction—0
Finding Good Representations of Emotions for Text Classification—0
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild—0
Multimodal Local-Global Ranking Fusion for Emotion Recognition—0
Multimodal Language Analysis with Recurrent Multistage FusionCode0
A Survey on Sentiment and Emotion Analysis for Computational Literary Studies—0
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets—0
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias—0
Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition—0
Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions—0
Hybrid Attention based Multimodal Network for Spoken Language Classification—0
CAKE: Compact and Accurate K-dimensional representation of Emotion—0
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data—0
Emotion Recognition from Speech based on Relevant Feature and Majority Voting—0
An Attention Model for group-level emotion recognitionCode0
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions—0
Real time P, QRS and T wave detection by QRS matched filter methodCode0
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling—0
SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues—0
Recognizing Emotions in Video Using Multimodal DNN Feature Fusion—0
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier—0
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words—0
Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System—0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—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