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A Dataset for Oral Reading in Young English Readers

Summary

Few English child speech corpora focus on oral reading, and those that do (notably the CMU Kids Corpus) have weaknesses. Their texts are not tied to a reading curriculum, their transcripts are not clearly manually verified, and their error labels are coarse. The authors address this with the Storiza Corpus. U.S. children in grades 1–3 who grow up with English read stories produced by the Storiza app, which uses GPT-4o-mini to write texts matched to the UFLI phonics curriculum and to each child's chosen topic. The app also logs clicks, highlights and reading progress. Trained linguistics students annotated every recording by hand. Annotations cover sentence- and word-level time alignment, orthographic and IPA transcriptions, and a detailed reading-error scheme. The main contribution is this curriculum-aligned resource, released together with its annotation guidelines. It supports work on child-specific ASR, forced alignment, miscue detection, and research on reading development and language acquisition.

Key points

  • Scale: 63 children (26 in grade 1, 24 in grade 2, 13 in grade 3; 37 girls, 25 boys, 1 unreported) produced 320 story recordings. These total about 9 hours and 33,612 words, with an average recording length of 1 min 29 s.
  • Error scheme: 10 error categories (e.g., Disfluency, Phonological, Orthographic, Grammatical, Visual tracking) are split into 26 sub-labels. This refines the CMU Kids and CSLU Kids schemes; for example, False Start is divided into Self-Correction and Stutter.
  • Generation: Stories were produced with GPT-4o-mini and illustrations with FLUX1-dev, aligned with UFLI lessons.
  • Annotation process: Eleven annotators worked in Label Studio from June to December 2025, first at the sentence level and then at the word level.
  • Availability: Parents consented to release 239 recordings (74.69%) through the Linguistic Data Consortium. Story texts, alignments, transcriptions, error annotations and guidelines are public on OSF.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.conll-main.35

@inproceedings{rose2026dataset,
  title = {{A Dataset for Oral Reading in Young English Readers}},
  author = {Rose, Madison and Bennie, Michael and Pagliai, Valeria and Karakis, Hatice Kubra and Shen, Qian and Tai, Xinyi and Leite, Walter L. and Liu, Zoey},
  booktitle = {Proceedings of the 30th Conference on Computational Natural Language Learning},
  year = {2026},
  doi = {10.18653/v1/2026.conll-main.35},
  url = {https://doi.org/10.18653/v1/2026.conll-main.35}
}

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