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 851–860 of 2041 papers

Title	Date	Tasks	Status
Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors	Mar 8, 2022	AttributeEmotion Classification	—Unverified
Beyond Classification: Towards Speech Emotion Reasoning with Multitask AudioLLMs	Jun 7, 2025	Emotion Recognitionspeech-recognition	—Unverified
Dynamic Resolution Guidance for Facial Expression Recognition	Apr 9, 2024	Emotion RecognitionFacial Expression Recognition	—Unverified
Ethics Sheets for AI Tasks	May 1, 2022	ArticlesEmotion Recognition	—Unverified
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals	May 27, 2024	Brain Computer InterfaceEEG	—Unverified
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition	Jul 6, 2023	Emotion RecognitionEnsemble Learning	—Unverified
An Attribute-Aligned Strategy for Learning Speech Representation	Jun 5, 2021	AttributeEmotion Recognition	—Unverified
Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognition	Mar 24, 2022	Arousal EstimationEmotion Recognition	—Unverified
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness	Nov 28, 2018	Adversarial AttackBIG-bench Machine Learning	—Unverified
3D Human Sensing, Action and Emotion Recognition in Robot Assisted Therapy of Children With Autism	Jun 1, 2018	Emotion Recognition	—Unverified

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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