Proceedings of the Sixth Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments in cooperation with the MENTAL.ai consortium
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.