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 831–840 of 2041 papers

Title	Date	Tasks	Status
EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model	May 1, 2024	De-identificationEmotion Recognition	—Unverified
Beyond Isolated Utterances: Conversational Emotion Recognition	Sep 13, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Enhancing Multimodal Affective Analysis with Learned Live Comment Features	Oct 21, 2024	Contrastive LearningEmotion Recognition	—Unverified
Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment	Dec 30, 2024	cross-modal alignmentEmotion Recognition	—Unverified
An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild	Oct 7, 2020	Deep LearningEmotion Recognition	—Unverified
Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features	Oct 16, 2024	Emotion RecognitionSelf-Supervised Learning	—Unverified
Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025	Jun 2, 2025	Audio TaggingEmotion Recognition	—Unverified
Enhancing Student Engagement in Online Learning through Facial Expression Analysis and Complex Emotion Recognition using Deep Learning	Nov 17, 2023	Emotion Recognition	—Unverified
標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In Chinese]	Oct 1, 2016	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Dynamic Time-Alignment of Dimensional Annotations of Emotion using Recurrent Neural Networks	Sep 21, 2022	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