Automated phonological analysis and treatment target selection using AutoPATT

Abstract

ABSTRACT Automated analyses of speech samples can offer improved accuracy and timesaving advantages that streamline clinical assessment for children with a suspected speech sound disorder. In this paper, we introduce AutoPATT, an automated tool for clinical analysis of speech samples. This free, open-source tool was developed as a plug-in for Phon and follows the procedures of the Phonological Analysis and Treatment Target Selection protocol, including extraction of a phonetic inventory, phonemic inventory with corresponding minimal pairs, and initial consonant cluster inventory. AutoPATT also provides suggestions for complex treatment targets using evidence-based guidelines. Automated analyses and target suggestions were compared to manual analyses of 25 speech samples from children with phonological disorder. Results indicate that AutoPATT inventory analyses are more accurate than manual analyses. However, treatment targets generated by AutoPATT should be viewed as suggestions and not used to substitute necessary clinical judgement in the target selection process.