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 1461–1470 of 2041 papers

Title	Date	Tasks	Status
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations	Nov 1, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations	Nov 11, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features	Oct 1, 2015	Emotion ClassificationEmotion Recognition	—Unverified
KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation	Jun 1, 2019	Emotion Recognition	—Unverified
Label-less Learning for Traffic Control in an Edge Network	Aug 29, 2018	Emotion Recognition	—Unverified
Ladder Networks for Emotion Recognition: Using Unsupervised Auxiliary Tasks to Improve Predictions of Emotional Attributes	Apr 28, 2018	AttributeDecoder	—Unverified
LanSER: Language-Model Supported Speech Emotion Recognition	Sep 7, 2023	Automatic Speech RecognitionEmotion Recognition	—Unverified
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition	Sep 15, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Large Vision-Language Models as Emotion Recognizers in Context Awareness	Jul 16, 2024	Emotion RecognitionIn-Context Learning	—Unverified
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition	Feb 4, 2024	Emotion RecognitionSpeech Emotion 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