SOTAVerified

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 1051–1075 of 2041 papers

TitleStatusHype
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition—0
Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations—0
Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition—0
Contextual Emotion Recognition using Large Vision Language Models—0
Contextualized Emotion Recognition in Conversation as Sequence Tagging—0
Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor—0
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks—0
Continuous Learning Based Novelty Aware Emotion Recognition System—0
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages—0
Continuous Multimodal Emotion Recognition Approach for AVEC 2017—0
Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognition—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models—0
ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework—0
Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts—0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability—0
Re-Parameterization of Lightweight Transformer for On-Device Speech Emotion Recognition—0
Representation learning through cross-modal conditional teacher-student training for speech emotion recognition—0
Representation Learning with Graph Neural Networks for Speech Emotion Recognition—0
Research on several key technologies in practical speech emotion recognition—0
Convolutional Neural Network for emotion recognition to assist psychiatrists and psychologists during the COVID-19 pandemic: experts opinion—0
Responsible AI: Gender bias assessment in emotion recognition—0
Retrofitting of Pre-trained Emotion Words with VAD-dimensions and the Plutchik Emotions—0
Reusing Neural Speech Representations for Auditory Emotion Recognition—0
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition—0
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All datasetsEmomusicRAVDESSEMOTICFER2013MaSaC_ERCMPEDMSP-PodcastSEEDคลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1M2D-CLAPEmoA77.4—Unverified
2M2D2EmoA76.7—Unverified
3M2DEmoA76.1—Unverified
4Jukebox (Pre-training: CALM)EmoA72.1—Unverified
5CLMR (Pre-training: contrastive)EmoA67.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+AttentionAccuracy86.7—Unverified
2MultiMAE-DERWAR83.61—Unverified
3Intermediate-Attention-FusionAccuracy81.58—Unverified
4Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedSTAccuracy80.08—Unverified
5ERANN-0-4Accuracy74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CAGETop-3 Accuracy (%)14.73—Unverified
2FocusCLIPTop-3 Accuracy (%)13.73—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy66.13—Unverified
#ModelMetricClaimedVerifiedStatus
1MaSaC-ERC-ZF1-score (Weighted)51.17—Unverified
#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy40.34—Unverified
#ModelMetricClaimedVerifiedStatus
1w2v2-L-robust-12Concordance correlation coefficient (CCC)0.64—Unverified
#ModelMetricClaimedVerifiedStatus
14D-aNNAccuracy96.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN1'"1—Unverified