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 1831–1840 of 2041 papers

Title	Date	Tasks	Status
Dimensional emotion recognition using visual and textual cues	May 3, 2018	Emotion Recognition	—Unverified
Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction	Jun 10, 2022	Emotion Recognitiontext-classification	—Unverified
Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer	Mar 23, 2021	Emotion Recognition	—Unverified
Discriminating Neutral and Emotional Speech using Neural Networks	Dec 1, 2014	Emotion ClassificationEmotion Recognition	—Unverified
Discriminative Multiple Canonical Correlation Analysis for Information Fusion	Feb 28, 2021	Emotion RecognitionHandwritten Digit Recognition	—Unverified
Discriminative training for Convolved Multiple-Output Gaussian processes	Feb 7, 2015	Activity RecognitionEmotion Recognition	—Unverified
Disentangling Prosody Representations with Unsupervised Speech Reconstruction	Dec 14, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Distributed Processing of Biosignal-Database for Emotion Recognition with Mahout	Sep 9, 2016	BIG-bench Machine LearningEmotion Recognition	—Unverified
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study	May 8, 2021	Action Unit DetectionAttribute	—Unverified
Divergent Emotional Patterns in Disinformation on Social Media? An Analysis of Tweets and TikToks about the DANA in Valencia	Jan 28, 2025	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