Towards a Speech Therapy Support System Based on Phonological Processes Early Detection

Abstract Phonological disorders are characterized by substitutions, insertion and/or deletions of sounds during the process of language acquisition, which are known as Phonological Processes (PPs). In the speech therapy domain, an early identification of PPs allows the diagnosis and treatment of various pathologies and may improve clinical tasks, however, there are few proposals that focus on the identification of PPs for supporting Speech-Language Pathologists (SLPs). Recent research applied Case-Based Reasoning (CBR) in medical domain to identify specific cases related to patients. Situation-Awareness (SA) is a technique that allows computing systems to adapt itself and respond to users or other systems according to environment information. Moreover, there is no indicative in related literature of CBR and SA being used for detecting PPs that may occur in pronunciation. In this paper, we introduce the union of SA and CBR, tied to machine learning algorithms for proposing a system to predict PPs, supporting specialists in their clinical decisions. To evaluate the system, we implemented it in a software architecture prototype and evaluated the prototypes using a knowledge base containing near one hundred thousand audio files, collected from more than 1,000 pronunciation assessments. The evaluation of the prototypes showed an accuracy over 93% in the prediction of PPs, resulting in a efficient tool for clinical decision support and therapeutic planning. We also presented a direct qualitative comparison between our approach and related work.