Harnessing automatic speech recognition to realise Sustainable Development Goals 3, 9, and 17 through interdisciplinary partnerships for children with communication disability.
To showcase how applications of automatic speech recognition (ASR) technology could help solve challenges in speech-language pathology practice with children with communication disability, and contribute to the realisation of the Sustainable Development Goals (SDGs). ASR technologies have been devel...
| Publicado en: | International Journal of Speech-Language Pathology Vol. 25; no. 1; pp. 125 - 130 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162512069&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162512069 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17549507 8NBH jtl: International Journal of Speech-Language Pathology issn: 17549507 maglogo: Y pubinfo: dt: Feb2023 vid: 25 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 162512069 160732926 162512069 162512069 10.1080/17549507.2022.2146194 162512069 ppf: 125 ppct: 5 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Harnessing automatic speech recognition to realise Sustainable Development Goals 3, 9, and 17 through interdisciplinary partnerships for children with communication disability. aug: au: Baker, Elise Li, Weicong Hodges, Rosemary Masso, Sarah Jones, Caroline Guo, Yi Alt, Mary Antoniou, Mark Afshar, Saeed Tosi, Katrina Munro, Natalie affil: School of Health Sciences, Western Sydney University, Campbelltown, Australia sug: subj: Sustainable Growth Voice Recognition Systems Automation Speech-Language Pathology Professional Practice Communicative Disorders Rehabilitation Information Technology Language Evaluation Communicative Disorders Diagnosis Child Speech Sample Evaluation Speech Production Measurement Feedback Speech-Language Pathologists Interprofessional Relations Collaboration Multilingualism Child: 6-12 years ab: To showcase how applications of automatic speech recognition (ASR) technology could help solve challenges in speech-language pathology practice with children with communication disability, and contribute to the realisation of the Sustainable Development Goals (SDGs). ASR technologies have been developed to address the need for equitable, efficient, and accurate assessment and diagnosis of communication disability in children by automating the transcription and analysis of speech and language samples and supporting dual-language assessment of bilingual children. ASR tools can automate the measurement of and help optimise intervention fidelity. ASR tools can also be used by children to engage in independent speech production practice without relying on feedback from speech-language pathologists (SLPs), thus bridging the long-standing gap between recommended and received intervention intensity. These innovative technologies and tools have been generated from interdisciplinary partnerships between SLPs, engineers, data scientists, and linguists. To advance equitable, efficient, and effective speech-language pathology services for children with communication disability, SLPs would benefit from integrating ASR solutions into their clinical practice. Ongoing interdisciplinary research is needed to further advance ASR technologies to optimise children's outcomes. This commentary paper focusses on industry, innovation and infrastructure (SDG 9) and partnerships for the goals (SDG 17). It also addresses SDG 1, SDG 3, SDG 4, SDG 8, SDG 10, SDG 11, and SDG 16. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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