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 701–710 of 2041 papers

Title	Date	Tasks	Status
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
Convolutional Speech Recognition with Pitch and Voice Quality Features	Sep 2, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video	Nov 13, 2017	Emotion ClassificationEmotion Recognition	—Unverified
A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles	Apr 26, 2020	Emotion Recognition	—Unverified
A Layer-Anchoring Strategy for Enhancing Cross-Lingual Speech Emotion Recognition	Jul 6, 2024	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information	Sep 12, 2018	Deep LearningEEG	—Unverified
Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data	May 17, 2018	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition	Dec 1, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts	Jun 1, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
ALAS: Measuring Latent Speech-Text Alignment For Spoken Language Understanding In Multimodal LLMs	May 26, 2025	cross-modal alignmentEmotion 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