This paper explores the optimization of telephone functionalities through voice interaction in the
Moore language, prevalent in Burkina Faso. Data gathered from 492 individuals in Ouagadougou,
representing diverse dialects and vocal intensities across age groups, informs the study. Employing
K-Nearest Neighbor (KNN), Random Forest (RF), and Recurrent Neural Networks (RNNs), the
analysis focuses on 29 Moore language commands, prioritizing practicality and user interaction. The
findings suggest promising prospects for RNNs, achieving a 63% accuracy in recognizing isolated
words. This success hints at potential advancements in RNNs, incorporating attention mechanisms
and end-to-end technology, catering to the voice-controlled mobile device needs of Moore speakers.

Isolated Word Recognition RNN