WOLOF ASR data on urban transport

This is document is to present a wolof speech recognition dataset collected and prepared by<br> BAAMTU Datamation (a senegalease company focused on using data to help companies to<br> leverage AI and Big Data ).<br> Motivation<br> In a country(i.e Senegal), where about 50% of the population is illiterate, using existing<br> technologies and applications that are designed to be used by people who can read is very<br> difficult for those people. Our Goal here is to use ASR technique on WOLOF to help the illiterate<br> persons to interact with apps with just with their voice in a language they can already speak (i.e<br> WOLOF).<br> We chose the urban transport use case for two reasons :<br> ● Many urban transport users can’t read nor speak french, so they can’t interact with<br> existing apps that help passengers to find a Bus for a given destination.<br> ● There is already an existing app in SENEGAL (i.e WeeGo) which help passengers to get<br> information about urban transport, so the goal here is to build an ASR model that will be<br> plugged into the App so illiterate people will be able to use it(the resulting model is<br> actually used by the WeeGo App).<br> Wolof is the most used language in SENEGAL.