Automated speech analysis tools for children’s speech production: A systematic literature review
Abstract
Purpose A systematic search and review of published studies was conducted on the use of automated speech analysis (ASA) tools for analysing and modifying speech of typically-developing children learning a foreign language and children with speech sound disorders to determine (i) types, attributes, and purposes of ASA tools being used; (ii) accuracy against human judgment; and (iii) performance as therapeutic tools. Method Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied. Across nine databases, 32 articles published between January 2007 and December 2016 met inclusion criteria: (i) focussed on children's speech; (ii) tools used for speech analysis or modification; and (iii) reporting quantitative data on accuracy. Result Eighteen ASA tools were identified. These met the clinical threshold of 80% agreement with human judgment when used as predictors of intelligibility, impairment severity, or error category. Tool accuracy was typically Conclusion ASA tools show promise for automated analysis and modification of children's speech production within assessment and therapeutic applications. Further work is needed to train automated systems with larger samples of speech to increase accuracy for assessment and therapeutic feedback.
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Even when a system is built upon principles that would theoretically have diagnostic or therapeutic potential, there are few studies showing that the systems are actually effective for these purposes.
“The third obstacle is that even when a system is built upon principles that would theoretically have diagnostic or therapeutic potential, there are few studies showing that the systems are actually effective for these purposes.”
From Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future (2025) · cites this paper as McKechnie et al., 2018
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 McKechnie et al., 2018
For automated speech analysis of speech sounds, clinical utility is demonstrated when classifiers show at least 80% agreement with human raters on incorrect sounds (specificity above .8); however, levels of agreement seen between expert raters on a given perceptual task may complicate the global feasibility of this benchmark.
“For automated speech analysis of speech sounds, McKechnie et al. (2018) have previously argued that clinical utility is demonstrated when classifiers show at least 80% agreement with human raters on incorrect sounds (i.e., specificity above .8);however, levels of agreement seen between expert raters on a given perceptual task may complicate the global feasibility of this benchmark (e.g., Klein et”
From Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future (2025) · cites this paper as McKechnie et al. (2018)
Cites
PEAKS – A system for the automatic evaluation of voice and speech disorders
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Automatic detection of articulation disorders in children with cleft lip and palate
Maier, A., Hönig, F., Bocklet, T., Nöth, E., Stelzle, F., Nkenke, E., & Schuster, M. (2009) · The Journal of the Acoustical Society of America · doi:10.1121/1.3216913 · Cited by 71 · see citing works
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Saeedi, S., Bouraghi, H., Seifpanahi, M.-S., & Ghazisaeedi, M. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4814945 · Cited by 48 · see citing works
Lammert, J. M., Roberts, A. C., McRae, K., Batterink, L. J., & Butler, B. E. (2025) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2024_jslhr-24-00515 · Cited by 17 · see citing works
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- Authors
- McKechnie, J., Ahmed, B., Gutierrez-Osuna, R., Monroe, P., McCabe, P., & Ballard, K. J.
- Affiliations
- The University of Sydney (AU)
- Texas A&M University at Qatar (QA)
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
- Corresponding author
- McKechnie, J.
- Venue
- International Journal of Speech-Language Pathology
- Publisher
- Informa UK Limited
- Published
- 2018-07-11
- Volume
- 20
- Issue
- 6
- Pages
- 583-598
- ISSN
- 1441-7049 (ISSN-L)
- 1754-9507
- 1754-9515
- OpenAlex
- W2833335833
- Cited by
- 54
- References
- 4
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 8 · 2023: 11 · 2022: 12 · 2021: 3 · 2020: 6 · 2019: 1 · 2018: 2
- FWCI
- 0.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Speech-Language Pathology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Qatar National Research Fund
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Keywords
- automated speech analysis, speech sound disorders, children's speech production, foreign language pronunciation, speech modification, intelligibility assessment, speech therapy
- MeSH terms
- Child, Humans, Speech-Language Pathology, Speech Production Measurement, Speech Sound Disorder
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