This work addresses the problem of helping speech therapists in interpreting results of tachistoscopes. These are instruments widely employed to diagnose speech and reading disorders. Roughly speaking, they work as follows. During a session, some strings of letters, which may or not correspond to existing words, are displayed to the patient for an amount of time set by the therapist. Next, the patient is asked for typing the read string. From the machine learning point of view, this raise an interesting problem of analyzing the sets of input and output words to evaluate the presence of a pathology.

A Machine Learning Model to Detect Speech and Reading Pathologies

Fassetti F.;
2020

Abstract

This work addresses the problem of helping speech therapists in interpreting results of tachistoscopes. These are instruments widely employed to diagnose speech and reading disorders. Roughly speaking, they work as follows. During a session, some strings of letters, which may or not correspond to existing words, are displayed to the patient for an amount of time set by the therapist. Next, the patient is asked for typing the read string. From the machine learning point of view, this raise an interesting problem of analyzing the sets of input and output words to evaluate the presence of a pathology.
978-3-030-49185-7
978-3-030-49186-4
Deep neural networks
Dyslexia
Tachistoscope
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/313087
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact