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 1151–1175 of 2041 papers

TitleStatusHype
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
Speech Emotion Recognition using Support Vector Machine—0
Speech Emotion Recognition via an Attentive Time-Frequency Neural Network—0
Speech Emotion Recognition via Contrastive Loss under Siamese Networks—0
結合非線性動態特徵之語音情緒辨識(Speech Emotion Recognition via Nonlinear Dynamical Features)[In Chinese]—0
Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations—0
Speech Emotion Recognition with Dual-Sequence LSTM Architecture—0
Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation—0
SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning—0
Speech & Song Emotion Recognition Using Multilayer Perceptron and Standard Vector Machine—0
Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition—0
Spontaneous Emotion Recognition from Facial Thermal Images—0
SSNCSE_NLP@TamilNLP-ACL2022: Transformer based approach for Emotion analysis in Tamil language—0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—0
Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition—0
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation—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