@inproceedings{petti-korhonen-2024-losst,
    title = "{L}o{SST}-{AD}: A Longitudinal Corpus for Tracking {A}lzheimer{'}s Disease Related Changes in Spontaneous Speech",
    author = "Petti, Ulla  and
      Korhonen, Anna",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.944",
    pages = "10813--10821",
    abstract = "Language-based biomarkers have shown promising results in differentiating those with Alzheimer{'}s disease (AD) diagnosis from healthy individuals, but the earliest changes in language are thought to start years or even decades before the diagnosis. Detecting these changes is critical to allow early interventions, but research into the earliest signs is challenging, as it requires large longitudinal datasets that are time-consuming and expensive to collect. There is a need for alternative methods for tracking longitudinal language change, including Natural Language Processing (NLP) and speech recognition technologies. We present a novel corpus that can enable this: a corpus of transcripts of public interviews with 20 famous figures, half of whom will eventually be diagnosed with AD, recorded over several decades. We evaluate the corpus by validating patterns of vocabulary richness changes known from literature, such as decline in noun frequency, word length, and several other features. We show that public data could be used to collect longitudinal datasets without causing extra stress for the participant, and that these data can adequately reflect longitudinal AD-related changes in vocabulary richness. Our corpus can provide a valuable starting point for the development of early detection tools and enhance our understanding of how AD affects language over time.",
}

Language-based biomarkers have shown promising results in differentiating those with Alzheimer{'}s disease (AD) diagnosis from healthy individuals, but the earliest changes in language are thought to start years or even decades before the diagnosis. Detecting these changes is critical to allow early interventions, but research into the earliest signs is challenging, as it requires large longitudinal datasets that are time-consuming and expensive to collect. There is a need for alternative methods for tracking longitudinal language change, including Natural Language Processing (NLP) and speech recognition technologies. We present a novel corpus that can enable this: a corpus of transcripts of public interviews with 20 famous figures, half of whom will eventually be diagnosed with AD, recorded over several decades. We evaluate the corpus by validating patterns of vocabulary richness changes known from literature, such as decline in noun frequency, word length, and several other features. We show that public data could be used to collect longitudinal datasets without causing extra stress for the participant, and that these data can adequately reflect longitudinal AD-related changes in vocabulary richness. Our corpus can provide a valuable starting point for the development of early detection tools and enhance our understanding of how AD affects language over time.
Petti, Ulla Korhonen, Anna
LoSST-AD: A Longitudinal Corpus for Tracking Alzheimer's Disease Related Changes in Spontaneous Speech