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 1201–1210 of 2041 papers

Title	Date	Tasks	Status
Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations	Jun 1, 2025	Emotion RecognitionRhythm	—Unverified
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation	Dec 23, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Spanish DAL: A Spanish Dictionary of Affect in Language	Jun 1, 2013	Emotion RecognitionMachine Translation	—Unverified
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application	Jan 19, 2016	Emotion Recognition	—Unverified
Spatial-Temporal Recurrent Neural Network for Emotion Recognition	May 12, 2017	EEGElectroencephalogram (EEG)	—Unverified
Spatiotemporal Networks for Video Emotion Recognition	Apr 3, 2017	ClassificationEmotion Recognition	—Unverified
Speaker Attentive Speech Emotion Recognition	Apr 15, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Speaker Characterization by means of Attention Pooling	May 7, 2024	Emotion RecognitionSpeaker Recognition	—Unverified
Speaker Emotion Recognition: Leveraging Self-Supervised Models for Feature Extraction Using Wav2Vec2 and HuBERT	Nov 5, 2024	Emotion Recognition	—Unverified
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation	Jun 7, 2022	DecoderEmotion 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