Gender Representation in Open Source Speech Resources

Mahault Garnerin (1, 2, 3) , Solange Rossato (2) , Laurent Besacier (2)

1 GETALP - Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole
2 LIG - Laboratoire d'Informatique de Grenoble
3 LIDILEM - LInguistique et DIdactique des Langues Étrangères et Maternelles

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.

Mots clés: speech resources; gender; metadata; open speech language resources (OpenSLR)

Domaines: Sciences de l'Homme et Société; Etudes sur le genre; Linguistique; Informatique [cs]; Intelligence artificielle [cs.AI]; Informatique et langage [cs.CL]

Mahault Garnerin, Solange Rossato, Laurent Besacier. Gender Representation in Open Source Speech Resources. 12th Conference on Language Resources and Evaluation (LREC 2020), May 2020, Marseille, France. pp.6599-6605. ⟨halshs-02899402⟩