Abstract
As a trend in the tech industry, human-machine interaction has increasingly become friendly and intuitive. In this context, our work presents the implementation of a voice command recognition system based on Machine Learning composed of an audio capture system, phoneme segmentation, description of voice commands from standardized power density histograms, classifier training, and functional tests on a graphical interface. The results showed that the best classification algorithm when implementing different classifiers was Support Vector Machine (SVM), with an efficiency of 95.4%, as expected, given its optimal structure.
Supported by Universidad de La Salle Bogotá.
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Arias-Otalora, DS., Florez, A., Mellizo, G., Rodríguez-Garavito, C.H., Romero, E., Tumialan, J.A. (2022). A Machine Learning Based Command Voice Recognition Interface. In: Figueroa-García, J.C., Franco, C., Díaz-Gutierrez, Y., Hernández-Pérez, G. (eds) Applied Computer Sciences in Engineering. WEA 2022. Communications in Computer and Information Science, vol 1685. Springer, Cham. https://doi.org/10.1007/978-3-031-20611-5_37
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DOI: https://doi.org/10.1007/978-3-031-20611-5_37
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