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 361–370 of 2041 papers

Title	Date	Tasks	Status
A study on cross-corpus speech emotion recognition and data augmentation	Jan 10, 2022	Cross-corpusData Augmentation	—Unverified
A Linked Data Model for Multimodal Sentiment and Emotion Analysis	Jul 1, 2015	Emotion RecognitionOpinion Mining	—Unverified
A Study of Language and Classifier-independent Feature Analysis for Vocal Emotion Recognition	Nov 14, 2018	Emotion Recognition	—Unverified
ASR-based Features for Emotion Recognition: A Transfer Learning Approach	May 23, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition	May 12, 2023	EEGElectroencephalogram (EEG)	—Unverified
Adapting WavLM for Speech Emotion Recognition	May 7, 2024	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages	Mar 28, 2022	DenoisingEmotion Classification	—Unverified
Conversational Emotion Analysis via Attention Mechanisms	Oct 24, 2019	Emotion Recognition	—Unverified
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition	May 25, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles	Apr 26, 2020	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