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 1876–1900 of 2041 papers

TitleStatusHype
Learning Kernels over Strings using Gaussian Processes—0
Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition—0
On the Challenges of Sentiment Analysis for Dynamic Events—0
Research on several key technologies in practical speech emotion recognition—0
Tweeting AI: Perceptions of Lay vs Expert Twitterati—0
Continuous Multimodal Emotion Recognition Approach for AVEC 2017—0
Group Affect Prediction Using Multimodal DistributionsCode0
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian ClassifiersCode0
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach—0
Group-level Emotion Recognition using Transfer Learning from Face IdentificationCode0
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets—0
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features—0
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus—0
LIPN-UAM at EmoInt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination—0
DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method—0
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity—0
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning—0
A Question Answering Approach for Emotion Cause Extraction—0
Photorealistic Facial Expression Synthesis by the Conditional Difference Adversarial Autoencoder—0
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition—0
Learning spectro-temporal features with 3D CNNs for speech emotion recognition—0
Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning—0
EmoTxt: A Toolkit for Emotion Recognition from TextCode0
Benchmarking Multimodal Sentiment Analysis—0
Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition—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