End-to-End Label Uncertainty Modeling in Speech Emotion Recognition Using Bayesian Neural Networks and Label Distribution Learning

Prabhu, Navin Raj; Lehmann‐Willenbrock, Nale; Gerkmann, Timo

doi:10.1109/taffc.2023.3283595

IEEE Trans. Affective Comput.

2024

DOI: 10.1109/taffc.2023.3283595

|View full text |Cite

End-to-End Label Uncertainty Modeling in Speech Emotion Recognition Using Bayesian Neural Networks and Label Distribution Learning

Navin Raj Prabhu¹,

Nale Lehmann‐Willenbrock

Timo Gerkmann³

Help me understand this report

View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Few Labels are Enough! Semi-supervised Graph Learning for Social Interaction

Corbellini,

Giraldo,

Varni

et al. 2023

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

View full text Add to dashboard Cite

Endowing machines with social intelligence is a fundamental goal of artificial social intelligence. Dealing with human-centered phenomena requires, however, a considerable amount of manually annotated data, making data annotation a costly and challenging task that hinders the training of supervised learning algorithms. In this study, we apply an approach grounded on Graph Convolutional Network (GCN) to alleviate the annotation burden. As a test bed, we select emergent states analysis with specific reference to the team potency. At first, we build the PO-TENCY dataset by fusing three datasets on social interaction. Next, we compute a set of multimodal features characterizing the social behavior of the team members and the team as one. Finally, we feed the POTENCY dataset to a semi-supervised GCN, trained on a binary node classification task, with variable amounts of labels. We show that GCN can assign team potency labels to an unlabeled team in the dataset by using only a few labeled examples (i.e., 10% of data), with performances comparable to or higher than those of two baseline algorithms carrying out the same task in a fully supervised way.

show abstract

Few Labels are Enough! Semi-supervised Graph Learning for Social Interaction

Corbellini,

Giraldo,

Varni

et al. 2023

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

End-to-End Label Uncertainty Modeling in Speech Emotion Recognition Using Bayesian Neural Networks and Label Distribution Learning

Cited by 1 publication

References 51 publications

Few Labels are Enough! Semi-supervised Graph Learning for Social Interaction

Few Labels are Enough! Semi-supervised Graph Learning for Social Interaction

Contact Info

Product

Resources

About