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
End-to-End Continuous Speech Emotion Recognition in Real-life Customer Service Call Center Conversations	Oct 2, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition	Sep 30, 2023	Active LearningEmotion Recognition	—Unverified
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection	Sep 29, 2023	BenchmarkingDiversity	—Unverified
MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection	Sep 27, 2023	Emotion RecognitionSentiment Analysis	CodeCode Available
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition	Sep 22, 2023	Emotion ClassificationEmotion Recognition	—Unverified
Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios	Sep 21, 2023	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available
Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Study	Sep 21, 2023	Emotion Recognition	—Unverified
The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains	Sep 21, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
A Discourse-level Multi-scale Prosodic Model for Fine-grained Emotion Analysis	Sep 21, 2023	Emotion RecognitionSpeech Synthesis	—Unverified
Ensembling Multilingual Pre-Trained Models for Predicting Multi-Label Regression Emotion Share from Speech	Sep 20, 2023	Emotion RecognitionEnsemble Learning	—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