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 531–540 of 2041 papers

Title	Date	Tasks	Status	Score
Speech Emotion Recognition Using Multi-hop Attention Mechanism	Apr 23, 2019	Emotion ClassificationEmotion Recognition	CodeCode Available	5
EmotionX-KU: BERT-Max based Contextual Emotion Classifier	Jun 27, 2019	Emotion RecognitionLanguage Modeling	CodeCode Available	5
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems	Sep 10, 2021	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	5
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain Adaptation	Nov 19, 2024	Domain AdaptationEEG	CodeCode Available	5
Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features	Sep 14, 2024	Emotion RecognitionSpeech 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 in the Wild using Deep Neural Networks and Bayesian Classifiers	Sep 12, 2017	Emotion RecognitionGeneral Classification	CodeCode Available	5
Analysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion Mining	Aug 7, 2023	Emotion RecognitionOpinion Mining	CodeCode Available	5
Emotion Recognition in Horses with Convolutional Neural Networks	May 25, 2021	Emotion Recognition	CodeCode Available	5
Emotion Recognition of the Singing Voice: Toward a Real-Time Analysis Tool for Singers	May 1, 2021	BIG-bench Machine LearningEmotion 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