A Machine Learning Approach for Dyslexia Detection Using Turkish Audio Records

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.