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Delayed Wh-Question Development in Children with Hearing Loss: Evidence for Morphosyntactic Vulnerability from Corpus-Based NLP and LLM Analyses

Paper · 2026 · Tong Wu

Summary

This paper asks whether English-speaking children with hearing loss (CHL) fall behind typically developing (TD) peers in using wh-questions. Unlike earlier studies built on elicitation tasks, it looks at spontaneous conversation. The data are parent–child transcripts from two CHILDES Clinical-Eng corpora, Nicholas and Ambrose. A Python pipeline cleaned the transcripts with rules, found seven wh-words with regular expressions, and used spaCy dependency parsing to measure:

  • how often wh-questions occur
  • how many different wh-types appear
  • utterance length
  • completeness, embedding, fronting and inversion
  • formulaic frames

Statistical models then examined effects of age and MLU. CHL asked fewer wh-questions, used fewer wh-types, embedded less often and produced more incomplete questions. The gaps were widest for complex forms such as embedded and canonically fronted questions. They held after controlling for MLU in words, which points to a specific weakness rather than a general delay. An exploratory GPT annotation comparison found the LLM handled global structure reasonably well but struggled with fine word-order checks. The findings support input-sensitive accounts and show where automated tools can be trusted for clinical child speech.

Key points

  • Data: Clinical English Nicholas and Ambrose corpora (CHILDES/TalkBank). Together they contain 64,814 child utterances, of which TD children produced 42,398 and CHL produced 22,416. Only *CHI: tier speech was kept.
  • Tooling: Python 3.11, spaCy v3.8.7, pandas, numpy, scipy and statsmodels. Embedding was detected via ccomp/xcomp/advcl dependents of a fixed list of matrix verbs. Wh-frequencies were normalized per 100 utterances.
  • Main finding: CHL lagged in both quantity and structure of wh-questions, especially embedded and fronted forms. These group differences remained after controlling for MLU.
  • GPT agreement with the parser-based annotation (Cohen's κ / accuracy) depended strongly on the dimension:
    • embedding: 0.770 / 0.975
    • completeness: 0.648 / 0.830
    • wh-detection: 0.424 / 0.910
    • inversion: 0.218 / 0.540
    • fronting: κ not reported because of class imbalance
  • Conclusion on methods: parser and rule-based approaches suit precise syntactic diagnostics, while the LLM is best treated as an exploratory aid. Limitations include using only two English corpora and simplified operational definitions.

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

 Institutions

🎓Leiden UniversityLeiden · Netherlands37 works · 1,408 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPsychology · 1,649 worksDevelopmental and Educational Psychology · 523 worksNeuroscience · 186 worksCognitive Neuroscience · 185 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as Tong Wu. 2026. Delayed Wh-Question Development in Children with Hearing Loss: Evidence for Morphosyntactic Vulnerability from Corpus-Based NLP and LLM Analyses. Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.healing-1.26

@inproceedings{wu2026delayed,
  title = {{Delayed Wh-Question Development in Children with Hearing Loss: Evidence for Morphosyntactic Vulnerability from Corpus-Based NLP and LLM Analyses}},
  author = {Tong Wu},
  booktitle = {Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.healing-1.26},
  url = {https://doi.org/10.18653/v1/2026.healing-1.26}
}

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