A Case-based Approach for Identifying Error Patterns in Children’s Speech

What is it about?

In this paper, we present a case-based approach that uses machine learning for predicting Phonological Processes, aiming to provide clinical support in the identification of error patterns in children’s speech. Case-Based Reasoning (CBR) covers a range of different methods for organizing, retrieving, and indexing knowledge from previous cases. Thus, this methodology has been successfully applied in medical domain, due to its human and intelligent properties to diagnose the case of a patient. Our method was evaluated through a speech corpus containing near one hundred thousand audio files, collected from pronunciation assessments performed by speech-language pathologists with more than 1,000 children. Using our knowledge base along with incremental learning, we obtained an accuracy of over 93% for predicting the PPs, showing the efficiency of our method for clinical decision support.

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Why is it important?

In the speech therapy domain, an early identification of speech sound disorders allows the diagnosis and treatment of various pathologies and may aid clinical decision-making.