Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917366937419776 |
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| author | Ryser, Anja Gao, Yingqiang Ebling, Sarah |
| author_facet | Ryser, Anja Gao, Yingqiang Ebling, Sarah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00780 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech Ryser, Anja Gao, Yingqiang Ebling, Sarah Computation and Language Artificial Intelligence 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. |
| title | Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.00780 |