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 1971–1980 of 2041 papers

Title	Date	Tasks	Status
openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit	May 22, 2016	Document ClassificationEmotion Recognition	CodeCode Available
Attention Based Fully Convolutional Network for Speech Emotion Recognition	Jun 5, 2018	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
A Dataset for Speech Emotion Recognition in Greek Theatrical Plays	Mar 27, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets	Jun 11, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Attention-Augmented End-to-End Multi-Task Learning for Emotion Prediction from Speech	Mar 29, 2019	Emotion RecognitionMulti-Task Learning	CodeCode Available
Attending to Emotional Narratives	Jul 8, 2019	Emotion RecognitionTime Series	CodeCode Available
Characterizing Sources of Uncertainty to Proxy Calibration and Disambiguate Annotator and Data Bias	Sep 20, 2019	Emotion Recognition	CodeCode Available
Evaluation Metrics for Automated Typographic Poster Generation	Feb 10, 2024	Emotion Recognition	CodeCode Available
An Attention Model for group-level emotion recognition	Jul 9, 2018	Emotion Recognitionmodel	CodeCode Available
Medical Face Masks and Emotion Recognition from the Body: Insights from a Deep Learning Perspective	Feb 20, 2023	Emotion Recognition	CodeCode Available

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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