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 101–110 of 2041 papers

Title	Date	Tasks	Status	Hype	Score
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition	Mar 21, 2024	Emotion RecognitionNeural Architecture Search	CodeCode Available	1	5
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition	Aug 4, 2023	Cross-corpusDomain Adaptation	CodeCode Available	1	5
A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition	Nov 3, 2021	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1	5
CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition	Apr 12, 2025	EEGEmotion Recognition	CodeCode Available	1	5
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain	Jun 1, 2022	BenchmarkingEmotion Recognition	CodeCode Available	1	5
A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition	Mar 28, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1	5
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection	Aug 7, 2023	Continual LearningEmotion Recognition	CodeCode Available	1	5
ChatGPT: Jack of all trades, master of none	Feb 21, 2023	AllChatbot	CodeCode Available	1	5
Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention	Jun 8, 2021	Emotion ClassificationEmotion Recognition	CodeCode Available	1	5
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents	May 4, 2020	BIG-bench Machine LearningEmotion Recognition	CodeCode Available	1	5

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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