@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Speech and Language Biomarkers of Neurodegenerative Conditions: Developing Cross-Linguistically Valid Tools for Automatic Analysis
%A Nowenstein, Iris E.
%A Stanojevic, Marija
%A Örnólfsson, Gunnar
%A Jónsdóttir, María Kristín
%A Simpson, Bill
%A Sorinas Nerin, Jennifer
%A Berg\thórsdóttir, Bryndís
%A Hannesdóttir, Kristín
%A Novikova, Jekaterina
%A Curcic, Jelena
%Y Kokkinakis, Dimitrios
%Y Fraser, Kathleen C.
%Y Themistocleous, Charalambos K.
%Y Fors, Kristina Lundholm
%Y Tsanas, Athanasios
%Y Ohman, Fredrik
%S 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
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F nowenstein-etal-2024-speech
%X 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.
%U https://aclanthology.org/2024.rapid-1.4
%P 26-33
Markdown (Informal)
[Speech and Language Biomarkers of Neurodegenerative Conditions: Developing Cross-Linguistically Valid Tools for Automatic Analysis](https://aclanthology.org/2024.rapid-1.4) (Nowenstein et al., RaPID-WS 2024)
ACL
- Iris E. Nowenstein, Marija Stanojevic, Gunnar Örnólfsson, María Kristín Jónsdóttir, Bill Simpson, Jennifer Sorinas Nerin, Bryndís Bergþórsdóttir, Kristín Hannesdóttir, Jekaterina Novikova, and Jelena Curcic. 2024. Speech and Language Biomarkers of Neurodegenerative Conditions: Developing Cross-Linguistically Valid Tools for Automatic Analysis. In 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, pages 26–33, Torino, Italia. ELRA and ICCL.