Reproducible Speech Research with the Artificial Intelligence--Ready PERCEPT Corpora

• Published in: Journal of speech, language, and hearing research Vol. 66; no. 6; pp. 1986 - 2009
• Main Authors: Benway, Nina R, Preston, Jonathan L, Hitchcock, Elaine, Rose, Yvan, Salekin, Asif, Liang, Wendy, McAllister, Tara
• Format: Journal Article
• Language: English
• Published: United States American Speech-Language-Hearing Association 20-06-2023
• Subjects: Adolescent; Allied Health Personnel; Articulation (Speech); Artificial Intelligence; Audio Equipment; Child; Clinical Diagnosis; Humans; Phonetics; Reproducibility of Results; Speech; Speech Disorders; Speech Language Pathology; Speech Sound Disorder; Technology Uses in Education; Training
• ISSN: 1092-4388; 1558-9102
• DOI: 10.1044/2023_JSLHR-22-00343

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 /[Voiced alveolar and postalveolar approximant]s/) 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.