Children's Articulation Practice in SpeechLP Across Two Months

Purpose. To describe practice and changes in accepted-card accuracy across two months among returning SpeechLP profiles. Method. This retrospective observational study used routine records from January 22 through October 7, 2026. The cohort comprised 101 profiles with full 60-day observation opportunity and matching sound targets in consecutive 30-day periods. Accepted-card proportions were averaged across matched sounds within profiles, then equally across profiles. Confidence intervals used 10,000 whole-profile bootstrap resamples. Small cells and complementary cells were suppressed. There was no study-specific consent or independent ethics review; the company confirmed permission under the app's terms and privacy policy. Result. Across 660 matched profile-and-sound pairs, mean accepted-card accuracy increased from 69.4% to 74.9% (+5.5 percentage points; 95% CI 0.4 to 10.4). Of these pairs, 278 increased, 177 decreased and 205 were unchanged. Mean sessions per profile declined from 6.73 to 3.44; mean practice days declined from 4.70 to 2.71. Conclusion. Accepted-card accuracy was higher in the second month in this selected returning cohort. Scores may reflect repeated utterances, auditory modeling and recognition/scoring changes; app version was not adjusted for. Unvalidated scoring and the absence of a comparison group prevent attribution to practice or interpretation as clinical articulation improvement. Disclosure. These findings are preliminary and descriptive. Acceptance rates reflect the app's automated speech recognition decisions, which have not been validated against SLP ratings, and may also reflect changes in app scoring over time. They should not be interpreted as clinical articulation accuracy, within-platform growth, or evidence of treatment efficacy. Author's original manuscript (preprint), not peer reviewed. Research contact: [email protected]. Manuscript date November 3, 2026; source observation window January 22–October 7, 2026. Mixed rights: CC BY 4.0 applies ONLY to aggregate-tables.csv, aggregate-tables.json, sound-results.csv and study-results.json. Rights in the manuscript, figures, proprietary software and source records are reserved. See Rights-Manifest.json for file-level terms. No participant-level records or analysis scripts are included.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-03
DOI
https://doi.org/10.5281/zenodo.23932484
Primary Topic
Language Development and Disorders
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Children's Articulation Practice in SpeechLP Across Two Months

Shail Silver, Matthew Siu, Alissa Fox, Mari Gottlieb et al.
Zenodo (CERN European Organization for Nuclear Research)
Language Development and Disorders
preprint

Children's Articulation Practice in SpeechLP Across Two Months

Shail Silver, Matthew Siu, Alissa Fox, Mari Gottlieb, Jabali Ndungu
preprint en

Abstract

Purpose. To describe practice and changes in accepted-card accuracy across two months among returning SpeechLP profiles. Method. This retrospective observational study used routine records from January 22 through October 7, 2026. The cohort comprised 101 profiles with full 60-day observation opportunity and matching sound targets in consecutive 30-day periods. Accepted-card proportions were averaged across matched sounds within profiles, then equally across profiles. Confidence intervals used 10,000 whole-profile bootstrap resamples. Small cells and complementary cells were suppressed. There was no study-specific consent or independent ethics review; the company confirmed permission under the app's terms and privacy policy. Result. Across 660 matched profile-and-sound pairs, mean accepted-card accuracy increased from 69.4% to 74.9% (+5.5 percentage points; 95% CI 0.4 to 10.4). Of these pairs, 278 increased, 177 decreased and 205 were unchanged. Mean sessions per profile declined from 6.73 to 3.44; mean practice days declined from 4.70 to 2.71. Conclusion. Accepted-card accuracy was higher in the second month in this selected returning cohort. Scores may reflect repeated utterances, auditory modeling and recognition/scoring changes; app version was not adjusted for. Unvalidated scoring and the absence of a comparison group prevent attribution to practice or interpretation as clinical articulation improvement. Disclosure. These findings are preliminary and descriptive. Acceptance rates reflect the app's automated speech recognition decisions, which have not been validated against SLP ratings, and may also reflect changes in app scoring over time. They should not be interpreted as clinical articulation accuracy, within-platform growth, or evidence of treatment efficacy. Author's original manuscript (preprint), not peer reviewed. Research contact: [email protected]. Manuscript date November 3, 2026; source observation window January 22–October 7, 2026. Mixed rights: CC BY 4.0 applies ONLY to aggregate-tables.csv, aggregate-tables.json, sound-results.csv and study-results.json. Rights in the manuscript, figures, proprietary software and source records are reserved. See Rights-Manifest.json for file-level terms. No participant-level records or analysis scripts are included.

Zenodo (CERN European Organization for Nuclear Research)
Language Development and Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon