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 161–170 of 2041 papers

Title	Date	Tasks	Status	Hype	Score
CLARA: Multilingual Contrastive Learning for Audio Representation Acquisition	Oct 18, 2023	Audio ClassificationContrastive Learning	CodeCode Available	1	5
ChatGPT: Jack of all trades, master of none	Feb 21, 2023	AllChatbot	CodeCode Available	1	5
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations	Jul 1, 2023	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1	5
Cluster-Level Contrastive Learning for Emotion Recognition in Conversations	Feb 7, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1	5
Codified audio language modeling learns useful representations for music information retrieval	Jul 12, 2021	Emotion RecognitionGenre classification	CodeCode Available	1	5
Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis	Sep 17, 2021	ArticlesEmotion Recognition	CodeCode Available	1	5
Affective Behaviour Analysis Using Pretrained Model with Facial Priori	Jul 24, 2022	Emotion Recognition	CodeCode Available	1	5
CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in Conversation	Jul 1, 2022	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1	5
Compact Graph Architecture for Speech Emotion Recognition	Aug 5, 2020	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1	5
Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention	Jun 8, 2021	Emotion ClassificationEmotion Recognition	CodeCode Available	1	5

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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