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 111–120 of 2041 papers

Title	Date	Tasks	Status	Hype	Score
Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transfer	Mar 26, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1	5
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN	May 5, 2022	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1	5
Contrast and Generation Make BART a Good Dialogue Emotion Recognizer	Dec 21, 2021	Contrastive LearningDecoder	CodeCode Available	1	5
Compact Graph Architecture for Speech Emotion Recognition	Aug 5, 2020	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1	5
A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS dataset	Dec 30, 2021	Autonomous DrivingEmotion Recognition	CodeCode Available	1	5
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation	Aug 26, 2021	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1	5
Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss	Oct 30, 2020	Emotion RecognitionWord Embeddings	CodeCode Available	1	5
EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning	Apr 25, 2024	Emotion ClassificationEmotion Recognition	CodeCode Available	1	5
Context De-confounded Emotion Recognition	Mar 21, 2023	Emotion Recognition	CodeCode Available	1	5
Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Dataset	Oct 9, 2021	Deep LearningEmotion 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