Digitally Supported Analysis of Spontaneous Speech (DigiSpon): Benchmarking NLP-Supported Language Sample Analysis of Swiss Children's Speech
This work addresses the problem of time-consuming LSA for speech-language pathologists, though it appears incremental as it builds on existing NLP methods without major breakthroughs.
The study tackled the labor-intensive nature of language sample analysis (LSA) for diagnosing developmental language disorder in children by introducing an NLP-based approach applied to transcribed speech from 119 Swiss children, with preliminary findings showing potential for more efficient diagnosis within a human-in-the-loop framework.
Language sample analysis (LSA) is a process that complements standardized psychometric tests for diagnosing, for example, developmental language disorder (DLD) in children. However, its labor-intensive nature has limited its use in speech-language pathology practice. We introduce an approach that leverages natural language processing (NLP) methods not based 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. The study aims to identify optimal practices that support speech-language pathologists in diagnosing DLD more efficiently within a human-in-the-loop framework, without relying on potentially unethical implementations that leverage commercial LLMs. Preliminary findings underscore the potential of integrating locally deployed NLP methods into the process of semi-automatic LSA.