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 1381–1390 of 2041 papers

Title	Date	Tasks	Status
Identifying Emotions from Walking using Affective and Deep Features	Jun 14, 2019	Emotion Recognition	—Unverified
Identifying Stable Patterns over Time for Emotion Recognition from EEG	Jan 10, 2016	BIG-bench Machine LearningEEG	—Unverified
Identifying Worry in Twitter: Beyond Emotion Analysis	Nov 1, 2020	Decision MakingEmotion Recognition	—Unverified
"I have vxxx bxx connexxxn!": Facing Packet Loss in Deep Speech Emotion Recognition	May 15, 2020	Data AugmentationEmotion Recognition	—Unverified
IIIT-H TEMD Semi-Natural Emotional Speech Database from Professional Actors and Non-Actors	May 1, 2020	Emotion Recognition	—Unverified
IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction	Jun 1, 2018	Emotion RecognitionPOS	—Unverified
IITK at SemEval-2024 Task 10: Who is the speaker? Improving Emotion Recognition and Flip Reasoning in Conversations via Speaker Embeddings	Apr 6, 2024	Emotion Recognition	—Unverified
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features	Sep 1, 2017	Emotion Recognitionfeature selection	—Unverified
I Know How You Feel: Emotion Recognition with Facial Landmarks	Apr 22, 2018	ClassificationEmotion Classification	—Unverified
Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems	Aug 20, 2023	Emotion RecognitionLanguage Modelling	—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