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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 971–980 of 2041 papers

Title	Date	Tasks	Status
SGED: A Benchmark dataset for Performance Evaluation of Spiking Gesture Emotion Recognition	Apr 28, 2023	Emotion RecognitionGesture Recognition	CodeCode Available
A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition	Apr 22, 2023	Emotion RecognitionSpeaker Recognition	—Unverified
AIRCADE: an Anechoic and IR Convolution-based Auralization Data-compilation Ensemble	Apr 18, 2023	Emotion RecognitionInformation Retrieval	CodeCode Available
Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations	Apr 17, 2023	ClassificationEmotion Recognition	CodeCode Available
Evaluation of Speaker Anonymization on Emotional Speech	Apr 15, 2023	Automatic Speech RecognitionEmotion Recognition	—Unverified
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition	Apr 14, 2023	Emotion ClassificationEmotion Recognition	—Unverified
Unsupervised Speech Representation Pooling Using Vector Quantization	Apr 8, 2023	Emotion Recognitionintent-classification	CodeCode Available
An Empirical Study and Improvement for Speech Emotion Recognition	Apr 8, 2023	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset	Apr 6, 2023	Emotion RecognitionFacial Emotion Recognition	CodeCode Available
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP	Apr 3, 2023	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