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 1751–1760 of 2041 papers

Title	Date	Tasks	Status
Emotion Recognition From Speech With Recurrent Neural Networks	Jan 27, 2017	Emotion Recognition	CodeCode Available
A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion	Jul 21, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
Data Augmentation for Emotion Detection in Small Imbalanced Text Data	Oct 25, 2023	Data AugmentationEmotion Recognition	CodeCode Available
VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition	Aug 24, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available
Emotion Recognition from Speech	Dec 22, 2019	Emotion ClassificationEmotion Recognition	CodeCode Available
Multi-Task Learning Framework for Emotion Recognition in-the-wild	Jul 19, 2022	Emotion RecognitionMulti-Task Learning	CodeCode Available
The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages	Oct 23, 2023	Emotion RecognitionFew-Shot Learning	CodeCode Available
Multi-attention Recurrent Network for Human Communication Comprehension	Feb 3, 2018	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available
Pretext Tasks selection for multitask self-supervised speech representation learning	Jul 1, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond	Nov 6, 2018	Action LocalizationData Augmentation	CodeCode Available

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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