@inproceedings{van-dijk-etal-2025-box,
    title = "Out of the Box, into the Clinic? Evaluating State-of-the-Art {ASR} for Clinical Applications for Older Adults",
    author = "Van Dijk, Bram  and
      Kuiper, Tiberon  and
      Aoulad si Ahmed, Sirin  and
      Lefebvre, Armel  and
      Johnson, Jake  and
      Duin, Jan  and
      Mooijaart, Simon  and
      Spruit, Marco",
    editor = "Blodgett, Su Lin  and
      Curry, Amanda Cercas  and
      Dev, Sunipa  and
      Li, Siyan  and
      Madaio, Michael  and
      Wang, Jack  and
      Wu, Sherry Tongshuang  and
      Xiao, Ziang  and
      Yang, Diyi",
    booktitle = "Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.hcinlp-1.7/",
    pages = "72--78",
    ISBN = "979-8-89176-353-1",
    abstract = "Voice-controlled interfaces can support older adults in clinical contexts {--} with chatbots being a prime example {--} but reliable Automatic Speech Recognition (ASR) for underrepresented groups remains a bottleneck. This study evaluates state-of-the-art ASR models on language use of older Dutch adults, who interacted with the Welzijn.AI chatbot designed for geriatric contexts. We benchmark generic multilingual ASR models, and models fine-tuned for Dutch spoken by older adults, while also considering processing speed. Our results show that generic multilingual models outperform fine-tuned models, which suggests recent ASR models can generalise well out of the box to real-world datasets. Moreover, our results indicate that truncating generic models is helpful in balancing the accuracy-speed trade-off. Nonetheless, we also find inputs which cause a high word error rate and place them in context."
}