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 1091–1100 of 2041 papers

Title	Date	Tasks	Status
Photorealistic Facial Expression Synthesis by the Conditional Difference Adversarial Autoencoder	Aug 30, 2017	Data AugmentationDecoder	—Unverified
Pitch-Synchronous Single Frequency Filtering Spectrogram for Speech Emotion Recognition	Aug 7, 2019	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation	Oct 30, 2020	Action RecognitionEmotion Interpretation	—Unverified
Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition	Oct 13, 2021	EEGEEG Emotion Recognition	—Unverified
Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition	May 9, 2021	Emotion RecognitionSubject Transfer	—Unverified
Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis	Mar 3, 2023	Emotion RecognitionKnowledge Distillation	—Unverified
Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competition	Jul 8, 2021	Emotion Recognition	—Unverified
Prior versus Contextual Emotion of a Word in a Sentence	Jul 1, 2012	Emotion RecognitionQuestion Answering	—Unverified
Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning	Nov 17, 2022	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Privacy-preserving Representation Learning for Speech Understanding	Oct 26, 2023	ClassificationEmotion 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