Applying Situation-Awareness for Recommending Phonological Processes in the Children’s Speech

What is it about?

In this paper, we present a novel method for predicting Phonological Processes (i.e., error patterns that might occur in children's speech), supporting speech therapists in the identification of speech disorders. Our approach uses Situation Awareness (SA) tied to machine learning (ML) to first classify the correctness in the pronunciation of a set of target words. Then, a second instance of ML uses scores calculated from mispelled words to predict Phonological Processes.

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Why is it important?

Situation-Awareness (SA) involves the correct interpretation of scenarios, allowing a system to respond to the observed environment in several domains. Speech therapy is an area where SA may provide benefits; however, the related literature generally is not concerned with identifying phonological processes (PPs) in pronunciation and their effects on the management of therapeutic tasks. An early identification of speech sound disorders allows the diagnosis and treatment of various pathologies and the reasoning about situations may aid clinical decision-making.