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 1926–1950 of 2041 papers

TitleStatusHype
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