Research Article
Voice Interaction in Moore Language Study on Isolated Word Recognition in Audio Samples
@INPROCEEDINGS{10.4108/eai.18-12-2023.2348169, author={Moumini Kabore and Rodrique Kafando and Aminata Sabane and Abdoul Kader Kabore and T\^{e}gawend\^{e} F. Bissyande}, title={Voice Interaction in Moore Language Study on Isolated Word Recognition in Audio Samples}, proceedings={Proceedings of the 6th Computer Science Research Days, JRI 2023, 18-20 December 2023, Ouagadougou, Burkina Faso}, publisher={EAI}, proceedings_a={JRI}, year={2024}, month={6}, keywords={isolated word recognition rnn mel-frequency cepstral coefficient}, doi={10.4108/eai.18-12-2023.2348169} }- Moumini Kabore
Rodrique Kafando
Aminata Sabane
Abdoul Kader Kabore
Tégawendé F. Bissyande
Year: 2024
Voice Interaction in Moore Language Study on Isolated Word Recognition in Audio Samples
JRI
EAI
DOI: 10.4108/eai.18-12-2023.2348169
Abstract
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.