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 431–440 of 2041 papers

Title	Date	Tasks	Status
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition	Sep 20, 2021	Contrastive LearningEEG	—Unverified
Contrastive Unsupervised Learning for Speech Emotion Recognition	Feb 12, 2021	Emotion RecognitionRepresentation Learning	—Unverified
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning	Aug 23, 2019	ClassificationEmotion Classification	—Unverified
Conversational Emotion Analysis via Attention Mechanisms	Oct 24, 2019	Emotion Recognition	—Unverified
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos	Jun 1, 2018	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas	May 1, 2018	Emotion RecognitionSpeech Recognition	—Unverified
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model	May 2, 2024	DenoisingEmotion Recognition	—Unverified
Converting Sentiment Annotated Data to Emotion Annotated Data	Dec 1, 2019	Emotion ClassificationEmotion Recognition	—Unverified
Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition	Dec 1, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Construction of Emotional Lexicon Using Potts Model	Oct 1, 2013	Emotion Recognitionmodel	—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