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 1451–1500 of 2041 papers

TitleStatusHype
Joint Deep Cross-Domain Transfer Learning for Emotion Recognition—0
Joint Emotion Analysis via Multi-task Gaussian Processes—0
Joint Emotion Label Space Modelling for Affect Lexica—0
Joint Learning for Emotion Classification and Emotion Cause Detection—0
JudithJeyafreedaAndrew@TamilNLP-ACL2022:CNN for Emotion Analysis in Tamil—0
KEVER^2: Knowledge-Enhanced Visual Emotion Reasoning and Retrieval—0
Fine-grained Early Frequency Attention for Deep Speaker Representation Learning—0
Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition—0
Knowledge-aware Bayesian Co-attention for Multimodal Emotion Recognition—0
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer—0
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations—0
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations—0
Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features—0
KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation—0
Label-less Learning for Traffic Control in an Edge Network—0
Ladder Networks for Emotion Recognition: Using Unsupervised Auxiliary Tasks to Improve Predictions of Emotional Attributes—0
LanSER: Language-Model Supported Speech Emotion Recognition—0
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition—0
Large Vision-Language Models as Emotion Recognizers in Context Awareness—0
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition—0
LDEB -- Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues—0
LEAPMood: Light and Efficient Architecture to Predict Mood with Genetic Algorithm driven Hyperparameter Tuning—0
Learning a Facial Expression Embedding Disentangled From Identity—0
Learning Affective Correspondence between Music and Image—0
Learning Annotation Consensus for Continuous Emotion Recognition—0
Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition—0
learning discriminative features from spectrograms using center loss for speech emotion recognition—0
Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition—0
Learning Emotional-Blinded Face Representations—0
Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech—0
Learning Grimaces by Watching TV—0
Learning Kernels over Strings using Gaussian Processes—0
Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition—0
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations—0
Learning Paralinguistic Features from Audiobooks through Style Voice Conversion—0
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis—0
Learning spectro-temporal features with 3D CNNs for speech emotion recognition—0
Learning Spontaneity to Improve Emotion Recognition In Speech—0
Learning Transferable Features for Speech Emotion Recognition—0
Learning Visual Emotion Representations From Web Data—0
Learning Word Ratings for Empathy and Distress from Document-Level User Responses—0
Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition—0
Leveraging Label Information for Multimodal Emotion Recognition—0
Leveraging Label Potential for Enhanced Multimodal Emotion Recognition—0
Leveraging LLMs with Iterative Loop Structure for Enhanced Social Intelligence in Video Question Answering—0
Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces—0
Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition—0
Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models—0
Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks—0
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—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