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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 526–550 of 2041 papers

TitleStatusHype
Parameter Efficient Finetuning for Speech Emotion Recognition and Domain Adaptation—0
EmoBench: Evaluating the Emotional Intelligence of Large Language ModelsCode2
Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru—0
Personalized Large Language ModelsCode2
Multi-Modal Emotion Recognition by Text, Speech and Video Using Pretrained Transformers—0
Persian Speech Emotion Recognition by Fine-Tuning Transformers—0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Evaluation Metrics for Automated Typographic Poster GenerationCode0
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts—0
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition—0
Graph Neural Networks in EEG-based Emotion Recognition: A Survey—0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—0
Are Paralinguistic Representations all that is needed for Speech Emotion Recognition?—0
FindingEmo: An Image Dataset for Emotion Recognition in the Wild—0
LRDif: Diffusion Models for Under-Display Camera Emotion Recognition—0
Neuromorphic Valence and Arousal Estimation—0
Real-time EEG-based Emotion Recognition Model using Principal Component Analysis and Tree-based Models for NeurohumanitiesCode0
AMuSE: Adaptive Multimodal Analysis for Speaker Emotion Recognition in Group Conversations—0
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction—0
Density Adaptive Attention is All You Need: Robust Parameter-Efficient Fine-Tuning Across Multiple ModalitiesCode1
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition—0
Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition—0
Self context-aware emotion perception on human-robot interaction—0
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation—0
TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in ConversationCode1
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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