Cited by 43Language Development and DisordersAssistive Technology in Communication and MobilityVoice and Speech Disorders

A Longitudinal Evaluation of Tablet-Based Child Speech Therapy with Apraxia World

Hair, Adam, Ballard, Kirrie J., Markoulli, Constantina, Monroe, Penelope, Mckechnie, Jacqueline, Ahmed, Beena, Gutierrez-Osuna, Ricardo·2021·ACM Transactions on Accessible Computing

Abstract

Digital games can make speech therapy exercises more enjoyable for children and increase their motivation during therapy. However, many such games developed to date have not been designed for long-term use. To address this issue, we developed Apraxia World, a speech therapy game specifically intended to be played over extended periods. In this study, we examined pronunciation improvements, child engagement over time, and caregiver and automated pronunciation evaluation accuracy while using our game over a multi-month period. Ten children played Apraxia World at home during two counterbalanced 4-week treatment blocks separated by a 2-week break. In one treatment phase, children received pronunciation feedback from caregivers and in the other treatment phase, utterances were evaluated with an automated framework built into the game. We found that children made therapeutically significant speech improvements while using Apraxia World, and that the game successfully increased engagement during speech therapy practice. Additionally, in offline mispronunciation detection tests, our automated pronunciation evaluation framework outperformed a traditional method based on goodness of pronunciation scoring. Our results suggest that this type of speech therapy game is a valid complement to traditional home practice.

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Crossref · 10.1145/3433607

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  • A word practice game that presents words in preset order without recommendations or predictions based on real-time learner performance likely represents a non-AI clinical technology, whereas one that automatically uses speech analysis to monitor performance and/or adapt recommendations likely represents clinical AI.

    “A word practice game that presents words in this preset order, without recommendations or predictions based on learner performance in real time, likely represents a non-AI clinical technology, whereas a word practice game that automatically uses speech analysis to monitor learner performance and/or adapt word practice recommendations based on learner performance would likely represent a clinical”
  • Say Bananas! can be considered an AI clinician due to its ability to recommend speech practice trials and use automatic speech analysis to provide feedback.

    “Similarly, Say Bananas! can be considered an AI clinician due to its ability to recommend speech practice trials during use of the program and use automatic speech analysis to provide the learner with feedback.”
  • Two systematic reviews examining automatic speech analysis for computerized speech sound learning systems available before December 2016 and February 2022 highlighted an overall lack of high-quality randomized controlled trials examining effectiveness/efficacy of the systems.

    “For example, two systematic reviews that specifically examine the use of automatic speech analysis for computerized speech sound learning systems available before December 2016 (McKechnie et al., 2018) and February 2022 (Deka et al., 2024) have highlighted an overall lack of high-quality randomized controlled trials examining effectiveness/efficacy of the systems (cf. the high-quality clinical tri”
  • Speaker-specific methods (e.g., template matching as in Hair et al., 2021) could be used to provide evidence that test set performance does not differ based on demographic factors for the individual speakers recruited for the study.

    “Alternately, speaker-specific methods (e.g., template matching as in Hair et al., 2021) could be used to provide evidence that test set performance does not differ based on demographic factors for the individual speakers recruited for the study.”
  • SayBananas! app involves tasks for the child to listen and produce selected target words, and provides feedback on target word accuracy as a correct/incorrect decision.

    “This app involves tasks for the child user to both listen and produce selected target words, and is provided with feedback on target word accuracy, as a correct/incorrect decision (Hair et al. 2021; McLeod et al. 2023).”

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References 74 · Cited by 43

Authors
Hair, A., Ballard, K. J., Markoulli, C., Monroe, P., Mckechnie, J., Ahmed, B., & Gutierrez-Osuna, R.
Affiliations
  • Texas A&M University (US)
  • The University of Sydney (AU)
  • UNSW Sydney (AU)
  • Texas A&M University, College Station, Texas, United States
Venue
ACM Transactions on Accessible Computing
Published
2021-03-18
Volume
14
Issue
1
Pages
1-26
ISSN
  • 1936-7228 (ISSN-L)
  • 1936-7236
OpenAlex
W3139237097
Cited by
43
References
74
Citations by year
2026: 6 · 2025: 14 · 2024: 12 · 2023: 4 · 2022: 5 · 2021: 1 · 2020: 1
FWCI
8.05
Type
journal article
Language
en
Open access
closed
Locations
ACM Transactions on Accessible Computing (publishedVersion, public-domain)
Funders
  • NPRP (8-293-2-124)
  • Qatar National Research Fund
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
  • Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
  • Health Sciences › Medicine › Physiology › Voice and Speech Disorders
Keywords
speech therapy games, childhood apraxia of speech, child engagement, caregiver feedback, mispronunciation detection

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