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 971–980 of 2041 papers

Title	Date	Tasks	Status
Emotion recognition by fusing time synchronous and time asynchronous representations	Oct 27, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
CNN-n-GRU: end-to-end speech emotion recognition from raw waveform signal using CNNs and gated recurrent unit networks	Mar 23, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices	Jul 22, 2017	Emotion ClassificationEmotion Recognition	—Unverified
Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation	May 15, 2023	Emotion ClassificationEmotion Recognition	—Unverified
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation	Feb 15, 2018	Data AugmentationEmotion Recognition	—Unverified
Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email	Jun 1, 2018	Emotion Recognition	—Unverified
Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning	Jul 12, 2023	Contrastive LearningEEG	—Unverified
CNN based music emotion classification	Apr 19, 2017	ClassificationEmotion Classification	—Unverified
Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities	Feb 2, 2025	Emotion Recognition	—Unverified
CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT	Dec 1, 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