DESIGN AND IMPLEMENTATION OF LEARNING TOOLS TO READ THE BRAILLE LETTERS BASED ON VOICE PROCESSING AND ARDUINO USING MEL FREQUENCY CEPSTRAL COEFFICIENT AND K-NEAREST NEIGHBOR METHOD

Abstract

Ability to read Braille is critical skill for blind students. Without the skill, blind students would encounter difficulties in their learning activities because most learning materials are written using the Braille system. The currently applied Braille learning system uses printed paper that is time consuming and pricey. This research attempts to develop a tool for helping the blinds to learn how to read braille letters. The tool processes inputs in the form of speech signal into a text by applying Mel Frequency Cepstral Coefficient (MFCC) as a feature extraction method and K- Nearest Neighbor (KNN) as a classifier method. The text will subsequently be transformed into Braille pattern by using Arduino UNO. The test results discover the combination of Mel Frequency Cepstral Coefficient and K-Nearest Neighbor method are able to recognize the speech signal of different alphabets with 87,3% accuracy. Furthermore, the computing time for alphabet recognitions decreases 85 % when the device is applied This finding helps the blind students to recognize the alphabets easily and faster.