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 841–850 of 2041 papers

Title	Date	Tasks	Status
Contextualized Emotion Recognition in Conversation as Sequence Tagging	Jul 1, 2020	Emotion ClassificationEmotion Recognition	—Unverified
Ensemble emotion recognizing with multiple modal physiological signals	Jan 1, 2020	ClassificationEEG	—Unverified
Ensemble knowledge distillation of self-supervised speech models	Feb 24, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Ensemble of Hankel Matrices for Face Emotion Recognition	Jul 14, 2015	Emotion ClassificationEmotion Recognition	—Unverified
Beyond Isolated Utterances: Conversational Emotion Recognition	Sep 13, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Entropy-Assisted Multi-Modal Emotion Recognition Framework Based on Physiological Signals	Sep 22, 2018	EEGElectroencephalogram (EEG)	—Unverified
An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild	Oct 7, 2020	Deep LearningEmotion Recognition	—Unverified
ESIHGNN: Event-State Interactions Infused Heterogeneous Graph Neural Network for Conversational Emotion Recognition	May 7, 2024	Emotion RecognitionGraph Neural Network	—Unverified
ESTformer: Transformer Utilizing Spatiotemporal Dependencies for Electroencaphalogram Super-resolution	Dec 3, 2023	channel selectionEEG	—Unverified
標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In Chinese]	Oct 1, 2016	Emotion RecognitionSpeech Emotion Recognition	—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