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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 1901–1950 of 2041 papers

TitleStatusHype
Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices—0
Automatic Recognition of Facial Displays of Unfelt Emotions—0
A breakthrough in Speech emotion recognition using Deep Retinal Convolution Neural Networks—0
Emotion Recognition in Context—0
Context-Dependent Sentiment Analysis in User-Generated VideosCode0
Progressive Neural Networks for Transfer Learning in Emotion RecognitionCode0
Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition—0
Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition—0
Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units—0
Deep Factorization for Speech Signal—0
Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech—0
Spatial-Temporal Recurrent Neural Network for Emotion Recognition—0
Word Affect Intensities—0
End-to-End Multimodal Emotion Recognition using Deep Neural NetworksCode0
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition—0
CNN based music emotion classification—0
Improved Human Emotion Recognition Using Symmetry of Facial Key Points with Dihedral Group—0
Spatiotemporal Networks for Video Emotion Recognition—0
CAT: Credibility Analysis of Arabic Content on Twitter—0
A Multi-View Sentiment Corpus—0
A Controlled Set-Up Experiment to Establish Personalized Baselines for Real-Life Emotion Recognition—0
Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth dataCode0
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural NetworkCode0
Emotion Recognition From Speech With Recurrent Neural NetworksCode0
Web-based Semantic Similarity for Emotion Recognition in Web Objects—0
Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition—0
A Bilingual Attention Network for Code-switched Emotion Prediction—0
Combining Heterogeneous User Generated Data to Sense Well-being—0
Selective Co-occurrences for Word-Emotion Association—0
A Computational Analysis of Mahabharata—0
A domain-agnostic approach for opinion prediction on speechCode0
Cosmopolitan Mumbai, Orthodox Delhi, Techcity Bangalore:Understanding City Specific Societal Sentiment—0
Crowdsourcing-based Annotation of Emotions in Filipino and English Tweets—0
The Effect of Gender and Age Differences on the Recognition of Emotions from Facial Expressions—0
Feelings from the Past---Adapting Affective Lexicons for Historical Emotion Analysis—0
Fusion of EEG and Musical Features in Continuous Music-emotion Recognition—0
Study on Feature Subspace of Archetypal Emotions for Speech Emotion Recognition—0
Emotion Distribution Learning from Texts—0
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems—0
Analyzing the Affect of a Group of People Using Multi-modal Framework—0
Learning Grimaces by Watching TV—0
Divide-and-Conquer based Ensemble to Spot Emotions in Speech using MFCC and Random Forest—0
Support Super-Vector Machines in Automatic Speech Emotion Recognition—0
標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In Chinese]—0
Event Based Emotion Classification for News Articles—0
Mining Call Center Conversations Exhibiting Similar Affective States—0
Distributed Processing of Biosignal-Database for Emotion Recognition with Mahout—0
Edge Based Grid Super-Imposition for Crowd Emotion Recognition—0
Personalization Effect on Emotion Recognition from Physiological Data: An Investigation of Performance on Different Setups and Classifiers—0
Augmenting Supervised Emotion Recognition with Rule-Based Decision Model—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