Automated phonological analysis and treatment target selection using AutoPATT

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.

KEYWORDS:

• Phonology
• phonological disorder
• automated assessment

Acknowledgments

We thank the members of the Phonological Typologies Lab at San Diego State University, especially Sarah Covington, Stephanie Nguyen, Brooke Hendricks, Taylor Kubo, Diana Carreon, and Teresa Roquet for their assistance with this project.

Disclosure statement

The fifth author is an author of the Phonological Analysis and Treatment Target (PATT) Selection protocol. The first, second, and fifth authors are authors of AutoPATT. The third and fourth authors are authors of Phon. These tools and software programs are distributed freely. The authors have no relevant financial disclosures.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

¹ Archival data were retrieved from the Gierut/Learnability Project collection of the IUScholarWorks repository at https://scholarworks.iu.edu/dspace/handle/2022/20061 The archival data were original to the Learnability Project and supported by grants from the National Institutes of Health to Indiana University (DC00433, RR7031K, DC00076, DC001694; PI: Gierut). The views expressed herein do not represent those of the National Institutes of Health, Indiana University, or the Learnability Project. The author(s) assume(s) sole responsibility for any errors, modifications, misapplications, or misinterpretations that may have been introduced in extraction or use of the archival data.

Additional information

Funding

This work was supported by the National Institutes of Health under Grants F31 DC017697, R21 DC01720, and R01 HD051698-11