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 1201–1225 of 2041 papers

TitleStatusHype
Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations—0
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation—0
Spanish DAL: A Spanish Dictionary of Affect in Language—0
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application—0
Spatial-Temporal Recurrent Neural Network for Emotion Recognition—0
Spatiotemporal Networks for Video Emotion Recognition—0
Speaker Attentive Speech Emotion Recognition—0
Speaker Characterization by means of Attention Pooling—0
Speaker Emotion Recognition: Leveraging Self-Supervised Models for Feature Extraction Using Wav2Vec2 and HuBERT—0
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation—0
Speaker-invariant Affective Representation Learning via Adversarial Training—0
Speaker Normalization for Self-supervised Speech Emotion Recognition—0
Speech and Text-Based Emotion Recognizer—0
Speech-Based Emotion Recognition: Feature Selection by Self-Adaptive Multi-Criteria Genetic Algorithm—0
Speech-Emotion Detection in an Indonesian Movie—0
Speech Emotion Recognition Based on CNN+LSTM Model—0
Speech Emotion Recognition Based on Multi-feature and Multi-lingual Fusion—0
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features—0
Speech Emotion Recognition Considering Local Dynamic Features—0
Breaking Resource Barriers in Speech Emotion Recognition via Data Distillation—0
Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare—0
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method—0
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks—0
Speech Emotion Recognition using Self-Supervised Features—0
Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring—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