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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 376–400 of 2041 papers

TitleStatusHype
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations—0
Are Generative Language Models Multicultural? A Study on Hausa Culture and Emotions using ChatGPT—0
EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion RecognitionCode2
Breaking Resource Barriers in Speech Emotion Recognition via Data Distillation—0
Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and ResultsCode1
Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition—0
FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversationsCode0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
DASB -- Discrete Audio and Speech Benchmark—0
Feature Fusion Based on Mutual-Cross-Attention Mechanism for EEG Emotion RecognitionCode2
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction TuningCode4
Double Multi-Head Attention Multimodal System for Odyssey 2024 Speech Emotion Recognition Challenge—0
Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare—0
What Does it Take to Generalize SER Model Across Datasets? A Comprehensive Benchmark—0
Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding ChallengesCode2
Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask LearningCode0
CLDTA: Contrastive Learning based on Diagonal Transformer Autoencoder for Cross-Dataset EEG Emotion Recognition—0
Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion TechniquesCode0
Exploring Self-Supervised Multi-view Contrastive Learning for Speech Emotion Recognition with Limited Annotations—0
Multimodal Emotion Recognition based on Facial Expressions, Speech, and EEG—0
Improving Language Models for Emotion Analysis: Insights from Cognitive Science—0
ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion DatasetsCode0
EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and BenchmarkCode3
PERSONA: An Application for Emotion Recognition, Gender Recognition and Age Estimation—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