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!
Púpọ̀ àwọn àká-ọ̀rọ̀ afọ̀ ni wọ́n kọ́ pẹ̀lú àpọ̀jù àwọn àbùdá ìdánimọ̀ kan tí àbájáde rẹ̀ sì pọ̀n sẹ́yìn <articleLink> akọ àti kòlàkòsagbe</articleLink>. Èwọ́hùn tàbí èdè àdúgbò tí ó ṣe é ṣe kí wọ́n má ní asojú nínú àwọn data ti a sàmúlò ló jẹ́ pé wọ́n jẹ́ ọ̀wọ́ àwọn ènìyàn tí à ti tẹ̀rì. Ọ̀pọ̀ ẹ̀rọ ni ó ń làkàkà láti dá ohùn obìnrin mọ̀. Ìdí nìyí tí ó fi jẹ́ pé nínú àká-ọ̀rọ̀ wa, a fẹ́ àmúlùmọ́là!