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 741–750 of 2041 papers

Title	Date	Tasks	Status
Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru	Feb 18, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network	Sep 9, 2021	ClusteringCross-corpus	—Unverified
Conditioning LLMs with Emotion in Neural Machine Translation	Aug 6, 2024	Emotion RecognitionMachine Translation	—Unverified
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition	Oct 8, 2021	Emotion RecognitionObject	—Unverified
Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?	Jun 2, 2025	Emotion RecognitionMamba	—Unverified
Context-Dependent Models for Predicting and Characterizing Facial Expressiveness	Dec 10, 2019	Emotion Recognition	—Unverified
Are Large Language Models More Empathetic than Humans?	Jun 7, 2024	Emotion Recognition	—Unverified
AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations	Apr 12, 2024	Emotion RecognitionRepresentation Learning	—Unverified
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition	Oct 28, 2020	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Are Generative Language Models Multicultural? A Study on Hausa Culture and Emotions using ChatGPT	Jun 27, 2024	DiversityEmotion 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