@inproceedings{nowenstein-etal-2024-speech,
    title = "Speech and Language Biomarkers of Neurodegenerative Conditions: Developing Cross-Linguistically Valid Tools for Automatic Analysis",
    author = {Nowenstein, Iris E.  and
      Stanojevic, Marija  and
      {\"O}rn{\'o}lfsson, Gunnar  and
      J{\'o}nsd{\'o}ttir, Mar{\'\i}a Krist{\'\i}n  and
      Simpson, Bill  and
      Sorinas Nerin, Jennifer  and
      Berg{\th}{\'o}rsd{\'o}ttir, Brynd{\'\i}s  and
      Hannesd{\'o}ttir, Krist{\'\i}n  and
      Novikova, Jekaterina  and
      Curcic, Jelena},
    editor = "Kokkinakis, Dimitrios  and
      Fraser, Kathleen C.  and
      Themistocleous, Charalambos K.  and
      Fors, Kristina Lundholm  and
      Tsanas, Athanasios  and
      Ohman, Fredrik",
    booktitle = "Proceedings of the Fifth Workshop on Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments @LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.rapid-1.4",
    pages = "26--33",
    abstract = "In the last decade, a rapidly growing body of studies has shown promising results for the automatic detection and extraction of speech and language features as biomarkers of neurodegenerative conditions such as Alzheimer{'}s disease. This has sparked great optimism and the development of various digital health tools, but also warnings regarding the predominance of English in the field and calls for linguistically diverse research as well as global, equitable access to novel clinical instruments. To automatically extract clinically relevant features from transcripts in low-resource languages, two approaches are possible: 1) utilizing a limited range of language-specific tools or 2) translating text to English and then extracting the features. We evaluate these approaches for part-of-speech (POS) rates in transcripts of recorded picture descriptions from a cross-sectional study of Icelandic speakers at different stages of Alzheimer{'}s disease and healthy controls. While the translation method merits further exploration, only a subset of the POS categories show a promising correspondence to the direct extraction from the Icelandic transcripts in our results, indicating that the translation method has to be linguistically validated at the individual POS category level.",
}

In the last decade, a rapidly growing body of studies has shown promising results for the automatic detection and extraction of speech and language features as biomarkers of neurodegenerative conditions such as Alzheimer{'}s disease. This has sparked great optimism and the development of various digital health tools, but also warnings regarding the predominance of English in the field and calls for linguistically diverse research as well as global, equitable access to novel clinical instruments. To automatically extract clinically relevant features from transcripts in low-resource languages, two approaches are possible: 1) utilizing a limited range of language-specific tools or 2) translating text to English and then extracting the features. We evaluate these approaches for part-of-speech (POS) rates in transcripts of recorded picture descriptions from a cross-sectional study of Icelandic speakers at different stages of Alzheimer{'}s disease and healthy controls. While the translation method merits further exploration, only a subset of the POS categories show a promising correspondence to the direct extraction from the Icelandic transcripts in our results, indicating that the translation method has to be linguistically validated at the individual POS category level.
Nowenstein, Iris E. Stanojevic, Marija {\"O}rn{\'o}lfsson, Gunnar J{\'o}nsd{\'o}ttir, Mar{\'\i}a Krist{\'\i}n Simpson, Bill Sorinas Nerin, Jennifer Berg{\th}{\'o}rsd{\'o}ttir, Brynd{\'\i}s Hannesd{\'o}ttir, Krist{\'\i}n Novikova, Jekaterina Curcic, Jelena
Speech and Language Biomarkers of Neurodegenerative Conditions: Developing Cross-Linguistically Valid Tools for Automatic Analysis