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 1891–1900 of 2041 papers

Title	Date	Tasks	Status
NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets	Sep 1, 2017	Emotion RecognitionSentiment Analysis	—Unverified
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features	Sep 1, 2017	Emotion Recognitionfeature selection	—Unverified
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity	Sep 1, 2017	Emotion Recognitionregression	—Unverified
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus	Sep 1, 2017	Emotion RecognitionGeneral Classification	—Unverified
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning	Sep 1, 2017	Emotion RecognitionSentence	—Unverified
Photorealistic Facial Expression Synthesis by the Conditional Difference Adversarial Autoencoder	Aug 30, 2017	Data AugmentationDecoder	—Unverified
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition	Aug 23, 2017	Emotion Recognition	—Unverified
Learning spectro-temporal features with 3D CNNs for speech emotion recognition	Aug 14, 2017	Emotion RecognitionSpeech Emotion Recognition	—Unverified
EmoTxt: A Toolkit for Emotion Recognition from Text	Aug 13, 2017	Emotion ClassificationEmotion Recognition	CodeCode Available
Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning	Aug 13, 2017	Cross-corpusEmotion 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