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 1911–1920 of 2041 papers

Title	Date	Tasks	Status
Learning Speech Emotion Representations in the Quaternion Domain	Apr 5, 2022	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices	Jan 24, 2022	Emotion Recognition	CodeCode Available
Transformer based neural networks for emotion recognition in conversations	May 18, 2024	Causal Language ModelingEmotion Classification	CodeCode Available
Where is Your Evidence: Improving Fact-checking by Justification Modeling	Nov 1, 2018	Argument MiningBIG-bench Machine Learning	CodeCode Available
NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level Preprocessing	Oct 1, 2018	Emotion RecognitionSentence	CodeCode Available
Leaving Some Facial Features Behind	Oct 29, 2024	ClassificationEmotion Classification	CodeCode Available
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition	Jul 23, 2023	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available
Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition	Dec 18, 2023	Data AugmentationEmotion Recognition	CodeCode Available
Leveraging Content and Acoustic Representations for Speech Emotion Recognition	Sep 9, 2024	Emotion RecognitionLanguage Modelling	CodeCode Available
Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled Samples	Aug 23, 2024	Contrastive LearningEmotion 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