Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10827
Title: A machine learning approach for dyslexia detection using Turkish audio records
Authors: Taş, Tuğberk
Bulbul, Muammed Abdullah
Haşimoğlu, Abas
Meral, Yavuz
Çalışkan, Yasin
Budağova, Günay
Kutlu, Mucahid
Keywords: Dyslexia
machine learning
detection
classification
audio records
Diagnosis
Issue Date: 2023
Publisher: Tubitak Scientific & Technological Research Council Turkey
Abstract: Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, we first create a unique dataset that includes audio recordings of 12 dyslexic children and 13 nondyslexic children in an 8-month period. We explore various machine learning algorithms such as KNN and SVM and use the following features: Mel-frequency cepstral coefficients, reading rate, reading accuracy, the ratio of missing words, and confidence scores of the speech-to-text process. In our experiments, we show that children with dyslexia can be detected with 95.63% accuracy even though we use single-sentence long audio records. In addition, we show that the prediction performance of our model is similar to that of the humans'. In this paper, we provide a preliminary study showing that detecting children with dyslexia based on their audio records is possible. Once the mobile application version of our model is developed, parents can easily check whether their children are likely to be dyslexic or not, and seek professional help accordingly.
URI: https://doi.org/10.55730/1300-0632.4024
https://hdl.handle.net/20.500.11851/10827
ISSN: 1300-0632
1303-6203
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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