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 1531–1540 of 2041 papers

Title	Date	Tasks	Status
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks	Jun 20, 2024	Automatic Speech RecognitionDecoder	—Unverified
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition	Jan 16, 2020	Emotion ClassificationEmotion Recognition	—Unverified
An Affective Situation Labeling System from Psychological Behaviors in Emotion Recognition	Nov 4, 2019	Emotion Recognition	—Unverified
An Algerian Corpus and an Annotation Platform for Opinion and Emotion Analysis	May 1, 2020	Author ProfilingEmotion Recognition	—Unverified
Analysis of Basic Emotions in Texts Based on BERT Vector Representation	Jan 21, 2021	Emotion Recognition	—Unverified
Analysis of constant-Q filterbank based representations for speech emotion recognition	Nov 29, 2022	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Analysis of Resource-efficient Predictive Models for Natural Language Processing	Nov 1, 2020	BIG-bench Machine LearningEmotion Recognition	—Unverified
Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME	Jun 11, 2025	Emotion RecognitionSentiment Analysis	—Unverified
Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation	Jul 8, 2024	Automatic Speech RecognitionEmotion Recognition	—Unverified
Analyzing the Affect of a Group of People Using Multi-modal Framework	Oct 12, 2016	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