@inproceedings{belmonte-etal-2024-automatic,
    title = "Automatic Detection of Rhythmic Features in Pathological Speech of {MCI} and Dementia Patients",
    author = "Belmonte, Marica  and
      Gagliardi, Gloria  and
      Kokkinakis, Dimitrios  and
      Tamburini, Fabio",
    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-1.5",
    pages = "34--44",
    abstract = "Linguistic alterations represent one of the prodromal signs of cognitive decline associated with Dementia. In recent years, a growing body of work has been devoted to the development of algorithms for the automatic linguistic analysis of both oral and written texts, for diagnostic purposes. The extraction of Digital Linguistic Biomarkers from patients{'} verbal productions can indeed provide a rapid, ecological, and cost-effective system for large-scale screening of the pathology. This article contributes to the ongoing research in the field by exploring a traditionally less studied aspect of language in Dementia, namely the rhythmic characteristics of speech. In particular, the paper focuses on the automatic detection of rhythmic features in Italian-connected speech. A landmark-based system was developed and evaluated to segment the speech flow into vocalic and consonantal intervals and to calculate several rhythmic metrics. Additionally, the reliability of these metrics in identifying Mild Cognitive Impairment and Dementia patients was tested.",
}

Linguistic alterations represent one of the prodromal signs of cognitive decline associated with Dementia. In recent years, a growing body of work has been devoted to the development of algorithms for the automatic linguistic analysis of both oral and written texts, for diagnostic purposes. The extraction of Digital Linguistic Biomarkers from patients{'} verbal productions can indeed provide a rapid, ecological, and cost-effective system for large-scale screening of the pathology. This article contributes to the ongoing research in the field by exploring a traditionally less studied aspect of language in Dementia, namely the rhythmic characteristics of speech. In particular, the paper focuses on the automatic detection of rhythmic features in Italian-connected speech. A landmark-based system was developed and evaluated to segment the speech flow into vocalic and consonantal intervals and to calculate several rhythmic metrics. Additionally, the reliability of these metrics in identifying Mild Cognitive Impairment and Dementia patients was tested.
Belmonte, Marica Gagliardi, Gloria Kokkinakis, Dimitrios Tamburini, Fabio
Automatic Detection of Rhythmic Features in Pathological Speech of MCI and Dementia Patients