用本地部署的NLP技术辅助儿童语言样本分析，提升发育性语言障碍诊断效率。
Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech
- 基于本地NLP模型分析119名瑞士德语区儿童的语音转录文本
- 初步结果显示可实现半自动语言样本分析，减轻人工负担
- 适合临床语言病理学家与技术团队协作优化诊断流程
语言样本分析（LSA）是辅助标准化心理测量工具诊断儿童发育性语言障碍（DLD）的重要手段，但其高劳动强度限制了在言语语言病理学实践中的应用。本研究提出一种不依赖商业大语言模型（LLM）的自然语言处理（NLP）方法，应用于瑞士德语区119名儿童的语音转录数据，涵盖典型与非典型语言发展群体。该初步研究旨在探索支持临床语言病理学家更高效诊断DLD的最佳实践，并强调将本地部署的NLP方法融入半自动LSA流程的潜力。
原文摘要 · Abstract (English)
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.
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