Gender Representation in Open Source Speech Resources
This addresses fairness and transparency issues in spoken language AI systems for researchers and developers, but is incremental as it focuses on analysis and recommendations rather than new methods.
The study tackled the problem of gender representation in open source speech corpora, finding that gender information is often unclear and balance varies with corpus characteristics like speech type and language resource level.
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.