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 1041–1050 of 2041 papers

Title	Date	Tasks	Status
NLP meets psychotherapy: Using predicted client emotions and self-reported client emotions to measure emotional coherence	Nov 22, 2022	DiagnosticEmotion Recognition	—Unverified
FAF: A novel multimodal emotion recognition approach integrating face, body and text	Nov 20, 2022	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues	Nov 20, 2022	Emotion RecognitionNatural Language Understanding	CodeCode Available
Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition	Nov 18, 2022	Brain Computer InterfaceEEG	—Unverified
Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning	Nov 17, 2022	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Temporal-spatial Representation Learning Transformer for EEG-based Emotion Recognition	Nov 16, 2022	EEGElectroencephalogram (EEG)	—Unverified
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer	Nov 16, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Deep Emotion Recognition in Textual Conversations: A Survey	Nov 16, 2022	BenchmarkingEmotion Recognition	CodeCode Available
Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches	Nov 15, 2022	Data AugmentationEmotion Recognition	CodeCode Available
Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset	Nov 14, 2022	Cross-corpusDomain Adaptation	—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