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 421–430 of 2041 papers

Title	Date	Tasks	Status
Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru	Feb 18, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias	Aug 7, 2018	Emotion Recognition	—Unverified
Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition	May 1, 2016	Emotion Recognition	—Unverified
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks	Nov 18, 2020	Emotion RecognitionFacial Expression Recognition	—Unverified
Continuous Learning Based Novelty Aware Emotion Recognition System	Jun 14, 2023	Emotion Recognition	—Unverified
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages	Mar 28, 2022	DenoisingEmotion Classification	—Unverified
A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis	Jun 6, 2023	Decision MakingEmotion Recognition	—Unverified
Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognition	Mar 24, 2022	Arousal EstimationEmotion Recognition	—Unverified
Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas	May 1, 2018	Emotion RecognitionSpeech Recognition	—Unverified
Construction of Emotional Lexicon Using Potts Model	Oct 1, 2013	Emotion Recognitionmodel	—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