@inproceedings{rauniyar-etal-2023-breaking,
    title = "Breaking Barriers: Exploring the Diagnostic Potential of Speech Narratives in {H}indi for {A}lzheimer{'}s Disease",
    author = "Rauniyar, Kritesh  and
      Shiwakoti, Shuvam  and
      Poudel, Sweta  and
      Thapa, Surendrabikram  and
      Naseem, Usman  and
      Nasim, Mehwish",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.clinicalnlp-1.4",
    doi = "10.18653/v1/2023.clinicalnlp-1.4",
    pages = "24--30",
    abstract = "Alzheimer{'}s Disease (AD) is a neurodegenerative disorder that affects cognitive abilities and memory, especially in older adults. One of the challenges of AD is that it can be difficult to diagnose in its early stages. However, recent research has shown that changes in language, including speech decline and difficulty in processing information, can be important indicators of AD and may help with early detection. Hence, the speech narratives of the patients can be useful in diagnosing the early stages of Alzheimer{'}s disease. While the previous works have presented the potential of using speech narratives to diagnose AD in high-resource languages, this work explores the possibility of using a low-resourced language, i.e., Hindi language, to diagnose AD. In this paper, we present a dataset specifically for analyzing AD in the Hindi language, along with experimental results using various state-of-the-art algorithms to assess the diagnostic potential of speech narratives in Hindi. Our analysis suggests that speech narratives in the Hindi language have the potential to aid in the diagnosis of AD. Our dataset and code are made publicly available at \url{https://github.com/rkritesh210/DementiaBankHindi}.",
}

Alzheimer{'}s Disease (AD) is a neurodegenerative disorder that affects cognitive abilities and memory, especially in older adults. One of the challenges of AD is that it can be difficult to diagnose in its early stages. However, recent research has shown that changes in language, including speech decline and difficulty in processing information, can be important indicators of AD and may help with early detection. Hence, the speech narratives of the patients can be useful in diagnosing the early stages of Alzheimer{'}s disease. While the previous works have presented the potential of using speech narratives to diagnose AD in high-resource languages, this work explores the possibility of using a low-resourced language, i.e., Hindi language, to diagnose AD. In this paper, we present a dataset specifically for analyzing AD in the Hindi language, along with experimental results using various state-of-the-art algorithms to assess the diagnostic potential of speech narratives in Hindi. Our analysis suggests that speech narratives in the Hindi language have the potential to aid in the diagnosis of AD. Our dataset and code are made publicly available at \url{https://github.com/rkritesh210/DementiaBankHindi}.
Rauniyar, Kritesh Shiwakoti, Shuvam Poudel, Sweta Thapa, Surendrabikram Naseem, Usman Nasim, Mehwish
Breaking Barriers: Exploring the Diagnostic Potential of Speech Narratives in Hindi for Alzheimer's Disease