# Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech

arXiv: https://arxiv.org/abs/2504.00780 · HTML: https://mlsanity.com/paper/2504.00780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German](https://mlsanity.com/paper/2103.11401/) (2021)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [SDS-200: A Swiss German Speech to Standard German Text Corpus](https://mlsanity.com/paper/2205.09501/) (2022)
- [Improving Zero-shot Cross-lingual Transfer between Closely Related Languages by injecting Character-level Noise](https://mlsanity.com/paper/2109.06772/) (2021)
- [Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings](https://mlsanity.com/paper/1805.08237/) (2018)
- [STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions](https://mlsanity.com/paper/2305.18855/) (2023)
- [Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text Corpus](https://mlsanity.com/paper/2010.02810/) (2020)
- [Does Whisper understand Swiss German? An automatic, qualitative, and human evaluation](https://mlsanity.com/paper/2404.19310/) (2024)
- [Swiss German Speech to Text system evaluation](https://mlsanity.com/paper/2207.00412/) (2022)
- [Enhancing deep neural networks with morphological information](https://mlsanity.com/paper/2011.12432/) (2020)
- [Dialectal Speech Recognition and Translation of Swiss German Speech to Standard German Text: Microsoft's Submission to SwissText 2021](https://mlsanity.com/paper/2106.08126/) (2021)
- [STTATTS: Unified Speech-To-Text And Text-To-Speech Model](https://mlsanity.com/paper/2410.18607/) (2024)
- [Modeling Composite Labels for Neural Morphological Tagging](https://mlsanity.com/paper/1810.08815/) (2018)

## Abstract (arXiv)

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.