Gender Representation in Open Source Speech Resources

Mahault Garnerin, Solange Rossato, Laurent Besacier


Abstract
With the rise of artificial intelligence (AI) and the growing use of deep-learning architectures, the question of ethics, transparency and fairness of AI systems has become a central concern within the research community. We address transparency and fairness in spoken language systems by proposing a study about gender representation in speech resources available through the Open Speech and Language Resource platform. We show that finding gender information in open source corpora is not straightforward and that gender balance depends on other corpus characteristics (elicited/non elicited speech, low/high resource language, speech task targeted). The paper ends with recommendations about metadata and gender information for researchers in order to assure better transparency of the speech systems built using such corpora.
Anthology ID:
2020.lrec-1.813
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6599–6605
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.813
DOI:
Bibkey:
Cite (ACL):
Mahault Garnerin, Solange Rossato, and Laurent Besacier. 2020. Gender Representation in Open Source Speech Resources. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 6599–6605, Marseille, France. European Language Resources Association.
Cite (Informal):
Gender Representation in Open Source Speech Resources (Garnerin et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.813.pdf