A Longitudinal Evaluation of Tablet-Based Child Speech Therapy with Apraxia World
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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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”
From Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future (2025) · cites this paper as Hair et al., 2021
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.”
From Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future (2025) · cites this paper as Hair et al., 2021
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”
From Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future (2025) · cites this paper as Hair et al., 2021
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.”
From Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future (2025) · cites this paper as Hair et al., 2021
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).”
From Self‐Supervised App‐Based Speech Training for Children With Speech Sound Disorder—A Single‐Case Experimental Design Study (2026) · cites this paper as Hair et al. 2021
Hair et al. (2021) studied 10 children and observed improved target sound accuracy and high engagement over a 4 plus 4 week intervention.
“The researchers observed improved target sound accuracy in the children and high levels of engagement throughout the extended intervention.”
From Self‐Supervised App‐Based Speech Training for Children With Speech Sound Disorder—A Single‐Case Experimental Design Study (2026) · cites this paper as Hair et al. 2021
The observed dose frequency ranging between four and seven sessions per week in the present study is comparable to the frequency of four sessions per week in Hair et al. (2021) and McLeod et al. (2023), and higher than the frequency of 2-3 sessions per week in Peterson et al. (2022).
“Regarding dose frequency, on the other hand, the observed frequency ranging between four and seven sessions per week in the present study, is comparable to the frequency of four sessions per week in Hair et al. (2021) and McLeod et al. (2023), and higher than the frequency of 2–3 sessions per week in Peterson et al. (2022).”
From Self‐Supervised App‐Based Speech Training for Children With Speech Sound Disorder—A Single‐Case Experimental Design Study (2026) · cites this paper as Hair et al. 2021
The results that intervention with Pop2TalkNordic was not successful for the participating children runs counter to the positive results reported in previous studies of app-based speech intervention for children with SSD.
“The results that intervention with Pop2TalkNordic was not successful for the participating children runs counter to the positive results reported in previous studies of app‐based speech intervention for children with SSD (Hair et al. 2021; McLeod et al. 2023; Peterson et al. 2022).”
From Self‐Supervised App‐Based Speech Training for Children With Speech Sound Disorder—A Single‐Case Experimental Design Study (2026) · cites this paper as Hair et al. 2021
Compared to the observed dose of 76 production efforts in Hair et al. (2021) and 200 in Peterson et al. (2022), the present study's dose is markedly lower, whereas it is more similar to the 45 observed in McLeod et al. (2023).
“Compared to the observed dose of 76 production efforts in Hair et al. (2021), and 200 in Peterson et al. (2022), it is markedly lower, whereas it is more similar to the 45 observed in McLeod et al. (2023).”
From Self‐Supervised App‐Based Speech Training for Children With Speech Sound Disorder—A Single‐Case Experimental Design Study (2026) · cites this paper as Hair et al. 2021
Pop2TalkNordic offers more auditory exposure to target words than opportunities to production compared to both staRt and SayBananas!.
“It should be noted, however, that there is a critical difference between these speech‐training tools, where Pop2TalkNordic offers more auditory exposure to target words than opportunities to production compared to both staRt and SayBananas!.”
From Self‐Supervised App‐Based Speech Training for Children With Speech Sound Disorder—A Single‐Case Experimental Design Study (2026) · cites this paper as Hair et al. 2021
The current study employed a shorter and less supervised form of intervention as compared to previous investigations.
“In terms of intervention delivery, the current study employed a shorter and less supervised form of intervention as compared to previous investigations.”
From Self‐Supervised App‐Based Speech Training for Children With Speech Sound Disorder—A Single‐Case Experimental Design Study (2026) · cites this paper as Hair et al. 2021
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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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