Early Dyslexia Evidences using Speech Features

Fernanda Ribeiro, Alvaro Pereira Jr., Débora Paiva, Luciana Alves, Andrea Bianchi

Abstract

The pathologies of the language are alterations in the reading of a text caused by traumatisms. Many people go untreated due to the lack of specific tools and the high cost of using proprietary software, however, new audio signal processing technologies can aid in the process of identifying genetic pathologies. In the past, a methodology was developed by medical specialists, which extracts characteristics from the reading of a text aloud and returns evidence of dyslexia. In this work, a new computational approach is described in order to automate serving as a tool for dyslexia indication efficiently. The analysis is done in recordings of the reading of pre-defined texts with school-age children, being extracted characteristics using specific methodologies. The indication of the probability of dyslexia is performed using a machine learning algorithm. The tests were performed comparing with the classification performed by the specialist, obtaining high accuracy on the evidence of dyslexia. The difference between the values of the automatically collected characteristics and the manually assigned was below 20% for most of the characteristics. Finally, the results show a very promising area for audio signal processing with respect to the aid to specialists in the decision making related to language pathologies.

Download


Paper Citation


in Harvard Style

Ribeiro F., Pereira Jr. A., Paiva D., Alves L. and Bianchi A. (2020). Early Dyslexia Evidences using Speech Features.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 640-647. DOI: 10.5220/0009574906400647


in Bibtex Style

@conference{iceis20,
author={Fernanda Ribeiro and Alvaro Pereira Jr. and Débora Paiva and Luciana Alves and Andrea Bianchi},
title={Early Dyslexia Evidences using Speech Features},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={640-647},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009574906400647},
isbn={978-989-758-423-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Early Dyslexia Evidences using Speech Features
SN - 978-989-758-423-7
AU - Ribeiro F.
AU - Pereira Jr. A.
AU - Paiva D.
AU - Alves L.
AU - Bianchi A.
PY - 2020
SP - 640
EP - 647
DO - 10.5220/0009574906400647