@inproceedings{lindsay-etal-2024-cross,
    title = "Cross-Lingual Examination of Language Features and Cognitive Scores From Free Speech",
    author = {Lindsay, Hali  and
      Albertin, Giorgia  and
      Schwed, Louisa  and
      Linz, Nicklas  and
      Tr{\"o}ger, Johannes},
    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-1.3",
    pages = "16--25",
    abstract = "Speech analysis is gaining significance for monitoring neurodegenerative disorders, but with a view of application in clinical practice, solid evidence of the association of language features with cognitive scores is still needed. A cross-linguistic investigation has been pursued to examine whether language features show significance correlation with two cognitive scores, i.e. Mini-Mental State Examination and ki:e SB-C scores, on Alzheimer{'}s Disease patients. We explore 23 language features, representative of syntactic complexity and semantic richness, extracted on a dataset of free speech recordings of 138 participants distributed in four languages (Spanish, Catalan, German, Dutch). Data was analyzed using the speech library SIGMA; Pearson{'}s correlation was computed with Bonferroni correction, and a mixed effects linear regression analysis is done on the significant correlated results. MMSE and the SB-C are found to be correlated with no significant differences across languages. Three features were found to be significantly correlated with the SB-C scores. Among these, two features of lexical richness show consistent patterns across languages, while determiner rate showed language-specific patterns.",
}

Speech analysis is gaining significance for monitoring neurodegenerative disorders, but with a view of application in clinical practice, solid evidence of the association of language features with cognitive scores is still needed. A cross-linguistic investigation has been pursued to examine whether language features show significance correlation with two cognitive scores, i.e. Mini-Mental State Examination and ki:e SB-C scores, on Alzheimer{'}s Disease patients. We explore 23 language features, representative of syntactic complexity and semantic richness, extracted on a dataset of free speech recordings of 138 participants distributed in four languages (Spanish, Catalan, German, Dutch). Data was analyzed using the speech library SIGMA; Pearson{'}s correlation was computed with Bonferroni correction, and a mixed effects linear regression analysis is done on the significant correlated results. MMSE and the SB-C are found to be correlated with no significant differences across languages. Three features were found to be significantly correlated with the SB-C scores. Among these, two features of lexical richness show consistent patterns across languages, while determiner rate showed language-specific patterns.
Lindsay, Hali Albertin, Giorgia Schwed, Louisa Linz, Nicklas Tr{\"o}ger, Johannes
Cross-Lingual Examination of Language Features and Cognitive Scores From Free Speech