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 1181–1190 of 2041 papers

Title	Date	Tasks	Status
Exploiting Multiple EEG Data Domains with Adversarial Learning	Apr 16, 2022	EEGElectroencephalogram (EEG)	CodeCode Available
Physically Disentangled Representations	Apr 11, 2022	AttributeClassification	CodeCode Available
Transformer-Based Self-Supervised Learning for Emotion Recognition	Apr 8, 2022	Emotion RecognitionSelf-Supervised Learning	—Unverified
Emotional Speech Recognition with Pre-trained Deep Visual Models	Apr 6, 2022	Emotion Recognitionspeech-recognition	CodeCode Available
Learning Speech Emotion Representations in the Quaternion Domain	Apr 5, 2022	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge	Apr 1, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations	Mar 31, 2022	Emotion Recognition	CodeCode Available
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition	Mar 31, 2022	Cross-corpusEmotion Recognition	—Unverified
Neural Architecture Search for Speech Emotion Recognition	Mar 31, 2022	Emotion RecognitionNeural Architecture Search	—Unverified
An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis	Mar 29, 2022	Cultural Vocal Bursts Intensity PredictionEEG	—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