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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2021–2030 of 2041 papers

Title	Date	Tasks	Status
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling	Feb 25, 2024	ChatbotDiversity	CodeCode Available
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition	Jun 21, 2022	Active LearningClustering	CodeCode Available
EmoTxt: A Toolkit for Emotion Recognition from Text	Aug 13, 2017	Emotion ClassificationEmotion Recognition	CodeCode Available
EmotionX-KU: BERT-Max based Contextual Emotion Classifier	Jun 27, 2019	Emotion RecognitionLanguage Modeling	CodeCode Available
EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation	Aug 17, 2019	Emotion RecognitionLanguage Modeling	CodeCode Available
Deep Learning based Emotion Recognition System Using Speech Features and Transcriptions	Jun 11, 2019	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions	May 24, 2022	Emotion RecognitionMulti-Task Learning	CodeCode Available
Modality-Collaborative Transformer with Hybrid Feature Reconstruction for Robust Emotion Recognition	Dec 26, 2023	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available
Deep Emotion Recognition in Textual Conversations: A Survey	Nov 16, 2022	BenchmarkingEmotion Recognition	CodeCode Available
Emotion Recognition Using Transformers with Masked Learning	Mar 19, 2024	Emotion Recognition	CodeCode Available

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All datasets Emomusic RAVDESS EMOTIC FER2013 MaSaC_ERC MPED MSP-Podcast SEED คลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	M2D-CLAP	EmoA	77.4	—	Unverified
2	M2D2	EmoA	76.7	—	Unverified
3	M2D	EmoA	76.1	—	Unverified
4	Jukebox (Pre-training: CALM)	EmoA	72.1	—	Unverified
5	CLMR (Pre-training: contrastive)	EmoA	67.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+Attention	Accuracy	86.7	—	Unverified
2	MultiMAE-DER	WAR	83.61	—	Unverified
3	Intermediate-Attention-Fusion	Accuracy	81.58	—	Unverified
4	Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedST	Accuracy	80.08	—	Unverified
5	ERANN-0-4	Accuracy	74.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CAGE	Top-3 Accuracy (%)	14.73	—	Unverified
2	FocusCLIP	Top-3 Accuracy (%)	13.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VGG based	5-class test accuracy	66.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MaSaC-ERC-Z	F1-score (Weighted)	51.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BiHDM	Accuracy	40.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	w2v2-L-robust-12	Concordance correlation coefficient (CCC)	0.64	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	4D-aNN	Accuracy	96.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	1'"	1	—	Unverified