[2504.00780] Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech

Computer Science > Computation and Language arXiv:2504.00780 (cs) [Submitted on 1 Apr 2025 (v1), last revised 30 Mar 2026 (this version, v2)] Title:Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech Authors:Anja Ryser, Yingqiang Gao, Sarah Ebling View a PDF of the paper titled Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech, by Anja Ryser and 2 other authors View PDF HTML (experimental) Abstract:Language sample analysis (LSA) is a process that complements standardized psychometric tests for diagnosing, for example, developmental language disorder (DLD) in children. However, its labour-intensive nature has limited its use in speech-language pathology practice. We introduce an approach that leverages natural language processing (NLP) methods that do not rely on commercial large language models (LLMs) applied to transcribed speech data from 119 children in the German-speaking part of Switzerland with typical and atypical language development. This preliminary study aims to identify optimal practices that support speech-language pathologists in diagnosing DLD more efficiently with active involvement of human specialists. Preliminary findings underscore the potential of integrating locally deployed NLP methods into the process of semi-automatic LSA. Comments: Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2504.00780 [cs.CL]   (or arXiv:2504.00780v2 [cs.CL] for this version) ...