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 871–880 of 2041 papers

Title	Date	Tasks	Status
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?	Nov 3, 2020	BIG-bench Machine LearningClassification	—Unverified
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos	Jun 1, 2018	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training	Oct 21, 2020	Domain AdaptationEmotion Recognition	—Unverified
Exploiting Facial Landmarks for Emotion Recognition in the Wild	Mar 30, 2016	Emotion Recognition	—Unverified
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems	Jun 16, 2021	Data AugmentationEmotion Recognition	—Unverified
Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset	Oct 3, 2019	Arousal EstimationEmotion Recognition	—Unverified
Converting Sentiment Annotated Data to Emotion Annotated Data	Dec 1, 2019	Emotion ClassificationEmotion Recognition	—Unverified
A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion Recognition	Jun 4, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese	Sep 23, 2020	Emotion Recognition	—Unverified
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models	Feb 20, 2020	Emotion RecognitionEvent Detection	—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