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ASR for treating apraxia of speech (Ballard et al., 2019)
figureposted on 2019-07-15, 21:28 authored by Kirrie J. Ballard, Nicole M. Etter, Songjia Shen, Penelope Monroe, Chek Tien Tan
Purpose: Individuals with neurogenic speech disorders require ongoing therapeutic support to achieve functional communication goals. Alternative methods for service delivery, such as tablet-based speech therapy applications, may help bridge the gap and bring therapeutic interventions to the patient in an engaging way. The purpose of this study was to evaluate an iPad-based speech therapy app that uses automatic speech recognition (ASR) software to provide feedback on speech accuracy to determine the ASR’s accuracy against human judgment and whether participants’ speech improved with this ASR-based feedback.
Method: Five participants with apraxia of speech plus aphasia secondary to stroke completed an intensive 4-week at-home therapy program using a novel word training app with built-in ASR. Multiple baselines across participants and behaviors designs were employed, with weekly probes and follow-up at 1 month posttreatment. Four sessions a week of 100 practice trials each were prescribed, with 1 being clinician-run and the remainder done independently. Dependent variables of interest were ASR–human agreement on accuracy during practice trials and human-judged word production accuracy over time in probes. Also, user experience surveys were completed immediately posttreatment.
Results: ASR–human agreement on accuracy averaged ~80%, which is a common threshold applied for interrater agreement. All participants demonstrated improved word production accuracy over time with the ASR-based feedback and maintenance of gains after 1 month. All participants reported enjoying using the app with support of a speech pathologist.
Conclusion: For these participants with apraxia of speech plus aphasia due to stroke, satisfactory gains were made in word production accuracy with an app-based therapy program providing ASR-based feedback on accuracy. Findings support further testing of this ASR-based approach as a supplement to clinician-run sessions to assist clients with similar profiles in achieving higher amount and intensity of practice as well as empowering them to manage their own therapy program.
Supplemental Material S1. Visualization of immediacy of effect for each participant for treated words (filled circles and red marking) and untreated words (unfilled circles and blue marking) across the baseline and treatment phases.
Ballard, K. J., Etter, N. M., Shen, S., Monroe, P., & Tan, C. T. (2019). Feasibility of automatic speech recognition for providing feedback during tablet-based treatment for apraxia of speech plus aphasia. American Journal of Speech-Language Pathology, 28, 818–834.
Publisher Note: This article is part of the Special Issue: Selected Papers From the 2018 Conference on Motor Speech—Clinical Science and Innovations.
This work was supported in part by National Health and Medical Research Council Grant 630489 and Australian Research Council Future Fellowship awarded to K. J. Ballard (FT120100355) and an Endeavour Postdoctoral Fellowship and Pennsylvania State University faculty funding awarded to N. M. Etter.
languagespeechapraxiaaphasiaapraxia of speechautomaticrecognitionfeedbacktablettreatmentneurogenicdisorderstherapytherapeuticcommunicationfunctionalservicedeliveryapplicationsappsinterventioniPadappsoftwarestrokeat homenovelwordtrainingclinicianindependentagreementaccuracyproductionmaintenancegainsspeech-language pathologistapp-basedsupplementassistLanguageLinguistic Processes (incl. Speech Production and Comprehension)Communication Technology and Digital Media Studies