SOTAVerified

Emotion Classification

Emotion classification, or emotion categorization, is the task of recognising emotions to classify them into the corresponding category. Given an input, classify it as 'neutral or no emotion' or as one, or more, of several given emotions that best represent the mental state of the subject's facial expression, words, and so on. Some example benchmarks include ROCStories, Many Faces of Anger (MFA), and GoEmotions. Models can be evaluated using metrics such as the Concordance Correlation Coefficient (CCC) and the Mean Squared Error (MSE).

Papers

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Showing 126–150 of 458 papers

TitleStatusHype
EigenEmo: Spectral Utterance Representation Using Dynamic Mode Decomposition for Speech Emotion Classification—0
Comparison of Gender- and Speaker-adaptive Emotion Recognition—0
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals—0
Emotion Intensity and its Control for Emotional Voice Conversion—0
Emotion recognition by fusing time synchronous and time asynchronous representations—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis—0
A Novel Dialect-Aware Framework for the Classification of Arabic Dialects and Emotions—0
CNN based music emotion classification—0
Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes—0
A Novel Approach for Effective Learning in Low Resourced Scenarios—0
A hierarchical approach with feature selection for emotion recognition from speech—0
Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification—0
AHD ConvNet for Speech Emotion Classification—0
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning—0
Emotion Classification In-Context in Spanish—0
Characteristic-Specific Partial Fine-Tuning for Efficient Emotion and Speaker Adaptation in Codec Language Text-to-Speech Models—0
Emotion Classification by Jointly Learning to Lexiconize and Classify—0
Emotion Carrier Recognition from Personal Narratives—0
Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG—0
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking—0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
Emotion Classification in Low and Moderate Resource Languages—0
Emotion Classification in Short English Texts using Deep Learning Techniques—0
Emotion Detection with Transformers: A Comparative Study—0
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All datasetsCMU-MOSEISemEval 2018 Task 1E-cEWALKMFAROCStoriesArmanEmoCAER-DynamicRAVDESSShortPersianEmo

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MARLIN (ViT-L)Accuracy80.63—Unverified
2MARLIN (ViT-B)Accuracy80.6—Unverified
3MARLIN (ViT-S)Accuracy80.38—Unverified
4ConCluGenAccuracy66.48—Unverified
#ModelMetricClaimedVerifiedStatus
1SpanEmoAccuracy0.6—Unverified
2BERT+DKAccuracy0.59—Unverified
3BERT-GCNAccuracy0.59—Unverified
4Transformer (finetune)Macro-F10.56—Unverified
#ModelMetricClaimedVerifiedStatus
1ProxEmo (ours)Accuracy82.4—Unverified
2STEP [bhattacharya2019step]Accuracy78.24—Unverified
3Baseline (Vanilla LSTM) [Ewalk]Accuracy55.47—Unverified
#ModelMetricClaimedVerifiedStatus
1MLKNNF-F1 score (Comb.)0.34—Unverified
2CC - XGBF-F1 score (Comb.)0.33—Unverified
#ModelMetricClaimedVerifiedStatus
1Semi-supervisionF165.88—Unverified
2NPN + Explanation TrainingF130.29—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep ParsBERTMacro F10.65—Unverified
#ModelMetricClaimedVerifiedStatus
1CAERNetAccuracy77.04—Unverified
#ModelMetricClaimedVerifiedStatus
1ERANN-0-4Top-1 Accuracy74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep ParsBERTMacro F10.71—Unverified