Authors:
Jim Radford
;
Gilles Richard
;
Hugo Richard
and
Mathieu Serrurier
Affiliation:
Dystech Ltd, Traralgon, Australia
Keyword(s):
Dyslexia, Screening, Machine Learning.
Abstract:
Dyslexia impacts the individual’s ability to read, interferes with academic achievements and may also have long term consequences beyond the learning years. Early detection is critical. It is usually done via a lengthy battery of tests: human experts score these tests to decide whether the child requires specific education strategies. This human assessment can also lead to inconsistencies. That is why there is a strong need for earlier, simpler (and cheaper) screening of dyslexia. In this paper, we investigate the potential of modern Artificial Intelligence in automating this screening. With this aim in mind and building upon previous works, we have gathered a dataset of audio recordings, from both non-dyslexic and dyslexic children. After proper preprocessing, we have applied diverse machine learning algorithms in order to check if some hidden patterns are discoverable, making a difference between dyslexic and non-dyslexic readers. Then, we built up our own neural network which outp
erforms the other tested approaches. Our results suggests the possibility to classify audio records as characteristic of dyslexia, leading to an accurate and inexpensive dyslexia screening via non-invasive methods, potentially reaching a large population for early intervention.
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