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 471–480 of 2041 papers

Title	Date	Tasks	Status	Score
ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets	Jun 11, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
BYEL : Bootstrap Your Emotion Latent	Jul 20, 2022	Emotion ClassificationEmotion Recognition	CodeCode Available	5
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from Speech	Jun 23, 2018	ClassificationEmotion Recognition	CodeCode Available	5
Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features	Sep 14, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark	May 9, 2016	BenchmarkingEmotion Recognition	CodeCode Available	5
Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions	May 24, 2022	Emotion RecognitionMulti-Task Learning	CodeCode Available	5
BSC-UPC at EmoSPeech-IberLEF2024: Attention Pooling for Emotion Recognition	Jul 17, 2024	Emotion RecognitionPosition	CodeCode Available	5
Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation	Feb 20, 2022	Domain AdaptationEEG	CodeCode Available	5
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators	Dec 2, 2019	Dialogue Act ClassificationEmotional Dialogue Acts	CodeCode Available	5
Bridging Modalities: Knowledge Distillation and Masked Training for Translating Multi-Modal Emotion Recognition to Uni-Modal, Speech-Only Emotion Recognition	Jan 4, 2024	Emotion RecognitionKnowledge Distillation	CodeCode Available	5

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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