Most speech databases are trained with an overrepresentation of certain demographics which results in a bias towards <articleLink>male and middle class</articleLink>. Accents and dialects that tend to be under-represented in training data sets are typically associated with groups of people who are already marginalised. Many machines also struggle to understand female voices. This is why in our voice database we want variety!
Tuget n taffiwin n yisefka n taɣect bnant ɣef kra n tdimugrafin, ayen i d-yeglan s kra n usmenyef i  <articleLink> irgazen d tneɣrit talemmast</articleLink>. Isusruyen akked tutlayin ur d-nettban ara  s waṭas d tid n yimdanen yuɣalen yakan deg rrif. Ihi, deg ṭaqa, timacinin ur zmirent ara ad ɛeqlent tuɣac n tlawin. Ɣef aya i nebɣa ddeqs n yisusruyen d yimdanen deg taffa-nneɣ n yisefka.