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 441–450 of 2041 papers

Title	Date	Tasks	Status
Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information	Sep 12, 2018	Deep LearningEEG	—Unverified
ALAS: Measuring Latent Speech-Text Alignment For Spoken Language Understanding In Multimodal LLMs	May 26, 2025	cross-modal alignmentEmotion Recognition	—Unverified
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video	Nov 13, 2017	Emotion ClassificationEmotion Recognition	—Unverified
Convolutional Speech Recognition with Pitch and Voice Quality Features	Sep 2, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas	May 1, 2018	Emotion RecognitionSpeech Recognition	—Unverified
CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate	Mar 10, 2023	Emotion RecognitionPosition	—Unverified
Construction of Emotional Lexicon Using Potts Model	Oct 1, 2013	Emotion Recognitionmodel	—Unverified
AI-Based Facial Emotion Recognition Solutions for Education: A Study of Teacher-User and Other Categories	Aug 29, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified
Cosmopolitan Mumbai, Orthodox Delhi, Techcity Bangalore:Understanding City Specific Societal Sentiment	Dec 1, 2016	Emotion Recognition	—Unverified
Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition	Apr 28, 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