GRB assessment of the Saarbrücken Voice Database
<p>GRB evaluations of the Saarbruecken database. Annex to the paper: <em>Multimodal and multi-output deep learning architectures for the automatic assessment of voice quality using the GRB scale. </em></p> <p>Published in IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 14, NO. 2, FEBRUARY 2020: <strong>DOI: </strong><a href="https://doi.org/10.1109/JSTSP.2019.2956410">10.1109/JSTSP.2019.2956410</a> <br> </p> <p>If you use these labels please cite as:</p> <p>J. D. Arias-Londoño, J. A. Gómez-García and J. I. Godino-Llorente, "Multimodal and Multi-Output Deep Learning Architectures for the Automatic Assessment of Voice Quality Using the GRB Scale," in <em>IEEE Journal of Selected Topics in Signal Processing</em>, vol. 14, no. 2, pp. 413-422, Feb. 2020.</p>
ShareScore
44/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 8
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4
Topics
Dataset details
- Repository
- zenodo
- Published
- November 21, 2019
- License
- cc-by-4.0
- DOI
- 10.5281/zenodo.3550735
Scored with release 0.9.0
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