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 1651–1660 of 2041 papers

Title	Date	Tasks	Status
Benchmarking Domain Generalization on EEG-based Emotion Recognition	Apr 18, 2022	BenchmarkingDomain Adaptation	—Unverified
Benchmarking Multimodal Sentiment Analysis	Jul 29, 2017	BenchmarkingEmotion Recognition	—Unverified
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation	Jan 17, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems	Apr 18, 2021	Adversarial AttackAutomatic Speech Recognition	—Unverified
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems	Jun 16, 2021	Data AugmentationEmotion Recognition	—Unverified
Better Spanish Emotion Recognition In-the-wild: Bringing Attention to Deep Spectrum Voice Analysis	Sep 8, 2024	Emotion Recognition	—Unverified
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals	May 27, 2024	Brain Computer InterfaceEEG	—Unverified
Beyond Classification: Towards Speech Emotion Reasoning with Multitask AudioLLMs	Jun 7, 2025	Emotion Recognitionspeech-recognition	—Unverified
Beyond Isolated Utterances: Conversational Emotion Recognition	Sep 13, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Modeling Challenging Patient Interactions: LLMs for Medical Communication Training	Mar 28, 2025	DiagnosticEmotion 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