PEEC: The Protected Entities Ethics Checklist for Collecting Speech Data From Vulnerable Clinical Populations.
Purpose: The rapid advancement of automatic speech recognition (ASR) and natural language processing technologies has created significant opportunities for clinical applications within speech and language disorders, yet these capabilities remain largely confined to high-resource languages and popula...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 69; no. 7; pp. 2997 - 3018 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
American Speech-Language-Hearing Association
Jul2026
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=195295661&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 195295661 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Jul2026 vid: 69 iid: 7 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 195295661 10.1044/2026_JSLHR-25-00540 ppf: 2997 ppct: 21 formats: fmt: @attributes: type: P size: 753KB tig: atl: PEEC: The Protected Entities Ethics Checklist for Collecting Speech Data From Vulnerable Clinical Populations. aug: au: Choi, Anna Seo Gyeong Cho, Sunghye Nowenstein, Iris affil: Department of Information Science, Cornell University, Ithaca, NY. Linguistic Data Consortium, Department of Linguistics, University of Pennsylvania, PA. Department of Icelandic and Comparative Cultural Studies, University of Iceland, Reykjavik. su: Cultural identity Documentation Artificial intelligence Privacy Socioeconomic factors Communicative disorders Informed consent (Medical law) Research ethics Medical ethics Transcultural medical care Automatic speech recognition Data management Speech-language pathology Natural language processing Acquisition of data Conceptual structures Cognition disorders Algorithms sug: subj: Cultural identity Documentation Artificial intelligence Privacy Socioeconomic factors Communicative disorders Informed consent (Medical law) Research ethics Medical ethics Transcultural medical care Automatic speech recognition Data management Speech-language pathology Natural language processing Acquisition of data Conceptual structures Cognition disorders Algorithms ab: Purpose: The rapid advancement of automatic speech recognition (ASR) and natural language processing technologies has created significant opportunities for clinical applications within speech and language disorders, yet these capabilities remain largely confined to high-resource languages and populations. As research communities work to address these inequities through inclusive speech data collection, the intersection of clinical vulnerability, linguistic diversity, and emerging speech and language technologies creates ethical considerations that are rarely addressed by existing guidelines. Ethical data collection practices affect the fairness and bias profiles of automatic speech and language analysis systems trained on these data, creating a foundational link between participant protection and algorithmic justice. Method: This article introduces the Protected Entities Ethics Checklist (PEEC), a comprehensive framework specifically designed for researchers collecting speech and language data from populations requiring enhanced protections. The framework addresses three core domains: participant protection and consent, data collection standards, and compliance implementation. Critically, the PEEC situates ethical data collection as a prerequisite for developing fair ASR systems, recognizing that procedural justice in research must precede algorithmic fairness. Results: The PEEC framework provides structured guidance for ethical research with protected entities including children, elderly adults with cognitive changes, individuals with communication disorders, and marginalized communities. It offers population-specific consent mechanisms, enhanced data protection measures, systematic quality assurance procedures, and explicit guidance on technical considerations for ASR applications while maintaining flexibility for diverse research contexts. Conclusions: Ethical treatment of research participants is inextricably linked to algorithmic fairness in speech technology development. The PEEC framework argues that procedural justice in data collection is a prerequisite for achieving fair AI systems, establishing the necessary ethical foundation for subsequent technological development in clinical speech research. By ensuring equitable and respectful data collection practices, we create the foundation for ASR systems that perform equitably across diverse populations. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|