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Cited by 12Language Development and DisordersVoice and Speech DisordersPhonetics and Phonology Research

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Reproducible Speech Research With the Artificial Intelligence–Ready PERCEPT Corpora

Open access
Benway, Nina R., Preston, Jonathan L., Hitchcock, Elaine, Rose, Yvan, Salekin, Asif, Liang, Wendy, McAllister, Tara·2023·Journal of Speech, Language, and Hearing Research
DOI 10.1044/2023_jslhr-22-00343PMC PMC12510240
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Abstract

Background: Publicly available speech corpora facilitate reproducible research by providing open-access data for participants who have consented/assented to data sharing among different research teams. Such corpora can also support clinical education, including perceptual training and training in the use of speech analysis tools. Purpose: In this research note, we introduce the PERCEPT (Perceptual Error Rating for the Clinical Evaluation of Phonetic Targets) corpora, PERCEPT-R (Rhotics) and PERCEPT-GFTA (Goldman-Fristoe Test of Articulation), which together contain over 36 hr of speech audio (> 125,000 syllable, word, and phrase utterances) from children, adolescents, and young adults aged 6–24 years with speech sound disorder (primarily residual speech sound disorders impacting /ɹ/) and age-matched peers. We highlight PhonBank as the repository for the corpora and demonstrate use of the associated speech analysis software, Phon, to query PERCEPT-R. A worked example of research with PERCEPT-R, suitable for clinical education and research training, is included as an appendix. Support for end users and information/descriptive statistics for future releases of the PERCEPT corpora can be found in a dedicated Slack channel. Finally, we discuss the potential for PERCEPT corpora to support the training of artificial intelligence clinical speech technology appropriate for use with children with speech sound disorders, the development of which has historically been constrained by the limited representation of either children or individuals with speech impairments in publicly available training corpora. Conclusions: We demonstrate the use of PERCEPT corpora, PhonBank, and Phon for clinical training and research questions appropriate to child citation speech. Increased use of these tools has the potential to enhance reproducibility in the study of speech development and disorders.

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  • There has not been enough publicly available audio or video examples of speech and language disorders to develop AI systems that work well for children or adults with these disorders.

    “First, and perhaps most critically, there has not been enough publicly available audio or video examples of speech and language disorders to develop AI systems that work well for children or adults with these types of disorders (e.g., Benway et al., 2023;Berisha et al., 2021;Shahin et al., 2020).”

    From Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future (2025) · cites this paper as Benway et al., 2023

    The Speech Technology and Ultrasuite section says large, labeled speech datasets for children with speech sound disorders are rare and that existing child mispronunciation-detection systems fall short of clinically acceptable accuracy. It does not establish the same shortage or performance limitation for adults or for language disorders, nor does it specifically address a lack of video examples. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • The development of ethical clinical AI is slowed by a lack of access to publicly available data sets that contain typical and clinical speech samples with a breadth and depth of linguistic and demographic representation.

    “The development of ethical clinical AI, however, is slowed by a lack of access to publicly available data sets that contain typical and clinical speech samples with a breadth and depth of linguistic and demographic representation (but note that this is a focus of many ongoing data collection projects, including Benway et al., 2023;Speights Atkins et al., 2020).”

    From Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future (2025) · cites this paper as Benway et al., 2023

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Research works printed but not linked: 38

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Metadata

Authors
Benway, N. R., Preston, J. L., Hitchcock, E., Rose, Y., Salekin, A., Liang, W., & McAllister, T.
Affiliations
  • Syracuse University (US)
  • Haskins Laboratories (US)
  • Montclair State University (US)
  • Memorial University of Newfoundland (CA)
  • New York University (US)
ORCID
Venue
Journal of Speech, Language, and Hearing Research
Publisher
American Speech Language Hearing Association
Published
2023-06-20
Volume
66
Issue
6
Pages
1986-2009
ISSN
  • 1092-4388 (ISSN-L)
  • 1558-9102
DOI
10.1044/2023_jslhr-22-00343
PMC
PMC12510240
OpenAlex
W4380785793
Cited by
12
References
2
Citations by year
2026: 1 · 2025: 3 · 2024: 5 · 2023: 3
FWCI
5.82
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Journal of Speech Language and Hearing Research (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • OSF Preprints (OSF Preprints) (submittedVersion)
  • OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
Funders
  • National Science Foundation (1541396, 1341006)
  • Syracuse University (ACI-1541396, ACI-1341006)
  • McGill University
  • National Institutes of Health (2015-2017, COVID-19)
  • National Institute on Deafness and Other Communication Disorders (R01DC017476)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD051698)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
  • Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
Keywords
speech sound disorders, rhotics, speech development, reproducible research, clinical teaching, speech corpus
MeSH terms
Adolescent, Artificial Intelligence, Child, Humans, Phonetics, Speech, Speech Disorders, Reproducibility of Results, Speech Sound Disorder

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