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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 251–300 of 2041 papers

TitleStatusHype
Disentangling Textual and Acoustic Features of Neural Speech RepresentationsCode1
Visual Prompting in LLMs for Enhancing Emotion Recognition—0
FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language ModelCode1
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis—0
Multi-Scale Temporal Transformer For Speech Emotion Recognition—0
Do Music Generation Models Encode Music Theory?Code1
EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record GenerationCode0
Two-stage Framework for Robust Speech Emotion Recognition Using Target Speaker Extraction in Human Speech Noise Conditions—0
Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis—0
UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion PerceptionCode0
Evaluation of OpenAI o1: Opportunities and Challenges of AGI—0
AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models—0
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised ModelsCode0
Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-LabelingCode0
Online Multi-level Contrastive Representation Distillation for Cross-Subject fNIRS Emotion RecognitionCode0
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition—0
Improving Emotion Recognition Accuracy with Personalized Clustering—0
CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification—0
Addressing Emotion Bias in Music Emotion Recognition and Generation with Frechet Audio DistanceCode3
Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error CorrectionCode0
Avengers Assemble: Amalgamation of Non-Semantic Features for Depression Detection—0
Strong Alone, Stronger Together: Synergizing Modality-Binding Foundation Models with Optimal Transport for Non-Verbal Emotion Recognition—0
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models—0
Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data SelectionCode0
Personalized Speech Emotion Recognition in Human-Robot Interaction using Vision Transformers—0
Stimulus Modality Matters: Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance—0
TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion RecognitionCode0
ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework—0
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition—0
Multi-Microphone and Multi-Modal Emotion Recognition in Reverberant Environment—0
Turbo your multi-modal classification with contrastive learning—0
Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic FeaturesCode0
PHemoNet: A Multimodal Network for Physiological SignalsCode2
Hierarchical Hypercomplex Network for Multimodal Emotion RecognitionCode2
Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better—0
Recent Trends of Multimodal Affective Computing: A Survey from NLP PerspectiveCode2
Multimodal Emotion Recognition with Vision-language Prompting and Modality Dropout—0
APEX: Attention on Personality based Emotion ReXgnition Framework—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
Leveraging Content and Acoustic Representations for Speech Emotion RecognitionCode0
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition—0
Better Spanish Emotion Recognition In-the-wild: Bringing Attention to Deep Spectrum Voice Analysis—0
Mamba-Enhanced Text-Audio-Video Alignment Network for Emotion Recognition in ConversationsCode1
Audio-Guided Fusion Techniques for Multimodal Emotion Analysis—0
Searching for Effective Preprocessing Method and CNN-based Architecture with Efficient Channel Attention on Speech Emotion Recognition—0
ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion RecognitionCode0
Progressive Residual Extraction based Pre-training for Speech Representation Learning—0
From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMsCode0
SpeechCaps: Advancing Instruction-Based Universal Speech Models with Multi-Talker Speaking Style CaptioningCode0
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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