Harnessing automatic speech recognition to realise sustainable development goals 3, 9, and 17 through interdisciplinary partnerships for children with communication disability

Purpose: 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). Result: 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. Conclusion: 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.