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 421–430 of 2041 papers

Title	Date	Tasks	Status	Score
Filter-based multi-task cross-corpus feature learning for speech emotion recognition	Feb 20, 2024	Cross-corpusEmotion Recognition	CodeCode Available	5
Group Affect Prediction Using Multimodal Distributions	Sep 17, 2017	Emotion RecognitionPrediction	CodeCode Available	5
Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias	Sep 20, 2019	Emotion Recognition	CodeCode Available	5
Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios	Sep 21, 2023	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	5
Exploiting Multiple EEG Data Domains with Adversarial Learning	Apr 16, 2022	EEGElectroencephalogram (EEG)	CodeCode Available	5
Leaving Some Facial Features Behind	Oct 29, 2024	ClassificationEmotion Classification	CodeCode Available	5
Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation	Apr 3, 2024	Emotion RecognitionLanguage Modelling	CodeCode Available	5
Leveraging Content and Acoustic Representations for Speech Emotion Recognition	Sep 9, 2024	Emotion RecognitionLanguage Modelling	CodeCode Available	5
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues	Nov 20, 2022	Emotion RecognitionNatural Language Understanding	CodeCode Available	5
Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations	Jun 27, 2023	Emotion Recognition	CodeCode Available	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