Detection of errors in the reading-aloud utilizing pattern recognition in speech for the early detection of dyslexia
DOI:
https://doi.org/10.24054/rcta.v1i39.1370Keywords:
Dyslexia, Speech Recognition, Pattern Recognition, Neural NetworksAbstract
This article presents the results of a comparison between three pattern recognition techniques applied to the classification of isolated words that simulates common errors that occur in the reading-aloud of children that manifest early stages of dyslexia. A database of recordings was made from five different speakers and sampled at 16 kHz, and later extended utilizing artificial data augmentation. The recordings were preprocessed to eliminate noise and the MFCC were later extracted. We compare the performance of the K-nearest neighbors (KNN), Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN), obtaining results in the order of 99 points for the recognition of phonetically distinctive words, and results in the order of 70 points for phonetically similar words.
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