Feasibility of automatic speech recognition for providing feedback during tablet-based treatment for apraxia of speech plus aphasia

Kirrie J. Ballard, Nicole Michele Etter, Songjia Shen, Penelope Monroe, Chek Tien Tand

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)818-834
Number of pages17
JournalAmerican journal of speech-language pathology
Volume28
Issue number2 Special Issue
DOIs
StatePublished - Jul 1 2019

Fingerprint

Apraxias
Aphasia
speech disorder
Tablets
Therapeutics
Speech Therapy
speech therapy
stroke
Speech Recognition Software
Recognition (Psychology)
Stroke
Speech Disorders
supplement

All Science Journal Classification (ASJC) codes

  • Otorhinolaryngology
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Speech and Hearing

Cite this

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title = "Feasibility of automatic speech recognition for providing feedback during tablet-based treatment for apraxia of speech plus aphasia",
abstract = "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.",
author = "Ballard, {Kirrie J.} and Etter, {Nicole Michele} and Songjia Shen and Penelope Monroe and Tand, {Chek Tien}",
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Feasibility of automatic speech recognition for providing feedback during tablet-based treatment for apraxia of speech plus aphasia. / Ballard, Kirrie J.; Etter, Nicole Michele; Shen, Songjia; Monroe, Penelope; Tand, Chek Tien.

In: American journal of speech-language pathology, Vol. 28, No. 2 Special Issue, 01.07.2019, p. 818-834.

Research output: Contribution to journalArticle

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