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 1131–1140 of 2041 papers

Title	Date	Tasks	Status
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability	Apr 3, 2021	Emotion Recognitionreinforcement-learning	—Unverified
Re-Parameterization of Lightweight Transformer for On-Device Speech Emotion Recognition	Nov 14, 2024	Emotion RecognitionModel Compression	—Unverified
Representation learning through cross-modal conditional teacher-student training for speech emotion recognition	Nov 30, 2021	Emotion ClassificationEmotion Recognition	—Unverified
Representation Learning with Graph Neural Networks for Speech Emotion Recognition	Aug 21, 2022	Emotion RecognitionGraph Neural Network	—Unverified
Research on several key technologies in practical speech emotion recognition	Sep 27, 2017	ClusteringEmotion Recognition	—Unverified
Convolutional Neural Network for emotion recognition to assist psychiatrists and psychologists during the COVID-19 pandemic: experts opinion	May 15, 2020	Computational EfficiencyEmotion Recognition	—Unverified
Responsible AI: Gender bias assessment in emotion recognition	Mar 21, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified
Retrofitting of Pre-trained Emotion Words with VAD-dimensions and the Plutchik Emotions	Dec 1, 2021	Emotion RecognitionWord Embeddings	—Unverified
Reusing Neural Speech Representations for Auditory Emotion Recognition	Mar 30, 2018	Emotion RecognitionGeneral Classification	—Unverified
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition	Jan 19, 2024	Contrastive LearningEmotion 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