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 631–640 of 2041 papers

Title	Date	Tasks	Status	Hype
Bias in Emotion Recognition with ChatGPT	Oct 18, 2023	Emotion RecognitionSentiment Analysis	—Unverified	0
DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations	Oct 17, 2023	BenchmarkingEmotion Recognition	CodeCode Available	1
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference	Oct 16, 2023	Bayesian InferenceEmotion Recognition	—Unverified	0
Crowdsourced and Automatic Speech Prominence Estimation	Oct 12, 2023	Emotion Recognitiontext-to-speech	CodeCode Available	1
Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals	Oct 11, 2023	EEGElectroencephalogram (EEG)	CodeCode Available	1
Towards Emotion-Based Synthetic Consciousness: Using LLMs to Estimate Emotion Probability Vectors	Oct 9, 2023	Emotion Recognition	—Unverified	0
Integrating Contrastive Learning into a Multitask Transformer Model for Effective Domain Adaptation	Oct 7, 2023	Contrastive LearningCross-corpus	—Unverified	0
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT	Oct 7, 2023	Audio captioningAutomatic Speech Recognition	CodeCode Available	2
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations	Oct 6, 2023	Emotion RecognitionTransfer Learning	—Unverified	0
In the Blink of an Eye: Event-based Emotion Recognition	Oct 6, 2023	Emotion Recognition	CodeCode Available	1

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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