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 541–550 of 2041 papers

Title	Date	Tasks	Status	Score
End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks	Oct 7, 2021	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Emotion Recognition in Horses with Convolutional Neural Networks	May 25, 2021	Emotion Recognition	CodeCode Available	5
Emotion recognition in talking-face videos using persistent entropy and neural networks	Oct 26, 2021	Emotion Recognition	CodeCode Available	5
Emotion Recognition From Speech With Recurrent Neural Networks	Jan 27, 2017	Emotion Recognition	CodeCode Available	5
Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News	Apr 21, 2024	BenchmarkingEmotion Recognition	CodeCode Available	5
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers	Sep 12, 2017	Emotion RecognitionGeneral Classification	CodeCode Available	5
Multi-Task Learning Framework for Emotion Recognition in-the-wild	Jul 19, 2022	Emotion RecognitionMulti-Task Learning	CodeCode Available	5
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation	Mar 20, 2023	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	5
Emotion Recognition from Speech	Dec 22, 2019	Emotion ClassificationEmotion Recognition	CodeCode Available	5
Emotion Detection From Tweets Using a BERT and SVM Ensemble Model	Aug 9, 2022	Emotion Recognition	CodeCode Available	5

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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