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Language-Independent Acoustic Biomarkers for Quantifying Speech Impairment in Huntington’s Disease.

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Title: Language-Independent Acoustic Biomarkers for Quantifying Speech Impairment in Huntington’s Disease.
Authors: Fahed, Vitória S.1,2 vitoria.fahed@ucdconnect.ie, Doheny, Emer P.1,2, Collazo, Carla3, Krzysztofik, Joanna4, Mann, Elliot5, Morgan-Jones, Philippa5,6, Mills, Laura5, Drew, Cheney5, Rosser, Anne E.7, Cousins, Rebecca8, Witkowski, Grzegorz4, Cubo, Esther3, Busse, Monica5, Lowery, Madeleine M.1,2
Source: American Journal of Speech-Language Pathology. May2024, Vol. 33 Issue 3, p1390-1405. 16p.
Subject Terms: *Reading, *Dysarthria, *Multilingualism, *Speech evaluation, *Speech disorders, *Factor analysis, Vowels, Smartphones, Research funding, Multiple regression analysis, Fisher exact test, Descriptive statistics, Physiological aspects of speech, Spanish language, Analysis of variance, English language, Huntington disease, Biomarkers
Abstract: Purpose: Changes in voice and speech are characteristic symptoms of Huntington’s disease (HD). Objective methods for quantifying speech impairment that can be used across languages could facilitate assessment of disease progression and intervention strategies. The aim of this study was to analyze acoustic features to identify language-independent features that could be used to quantify speech dysfunction in English-, Spanish-, and Polish-speaking participants with HD. Method: Ninety participants with HD and 83 control participants performed sustained vowel, syllable repetition, and reading passage tasks recorded with previously validated methods using mobile devices. Language-independent features that differed between HD and controls were identified. Principal component analysis (PCA) and unsupervised clustering were applied to the language-independent features of the HD data set to identify subgroups within the HD data. Results: Forty-six language-independent acoustic features that were significantly different between control participants and participants with HD were identified. Following dimensionality reduction using PCA, four speech clusters were identified in the HD data set. Unified Huntington’s Disease Rating Scale (UHDRS) total motor score, total functional capacity, and composite UHDRS were significantly different for pairwise comparisons of subgroups. The percentage of HD participants with higher dysarthria score and disease stage also increased across clusters. Conclusion: The results support the application of acoustic features to objectively quantify speech impairment and disease severity in HD in multilanguage studies. [ABSTRACT FROM AUTHOR]
Copyright of American Journal of Speech-Language Pathology is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Language-Independent Acoustic Biomarkers for Quantifying Speech Impairment in Huntington’s Disease.
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  Data: <searchLink fieldCode="AR" term="%22Fahed%2C+Vitória+S%2E%22">Fahed, Vitória S.</searchLink><relatesTo>1,2</relatesTo><i> vitoria.fahed@ucdconnect.ie</i><br /><searchLink fieldCode="AR" term="%22Doheny%2C+Emer+P%2E%22">Doheny, Emer P.</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Collazo%2C+Carla%22">Collazo, Carla</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Krzysztofik%2C+Joanna%22">Krzysztofik, Joanna</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Mann%2C+Elliot%22">Mann, Elliot</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Morgan-Jones%2C+Philippa%22">Morgan-Jones, Philippa</searchLink><relatesTo>5,6</relatesTo><br /><searchLink fieldCode="AR" term="%22Mills%2C+Laura%22">Mills, Laura</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Drew%2C+Cheney%22">Drew, Cheney</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Rosser%2C+Anne+E%2E%22">Rosser, Anne E.</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Cousins%2C+Rebecca%22">Cousins, Rebecca</searchLink><relatesTo>8</relatesTo><br /><searchLink fieldCode="AR" term="%22Witkowski%2C+Grzegorz%22">Witkowski, Grzegorz</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Cubo%2C+Esther%22">Cubo, Esther</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Busse%2C+Monica%22">Busse, Monica</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Lowery%2C+Madeleine+M%2E%22">Lowery, Madeleine M.</searchLink><relatesTo>1,2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22American+Journal+of+Speech-Language+Pathology%22">American Journal of Speech-Language Pathology</searchLink>. May2024, Vol. 33 Issue 3, p1390-1405. 16p.
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  Data: *<searchLink fieldCode="DE" term="%22Reading%22">Reading</searchLink><br />*<searchLink fieldCode="DE" term="%22Dysarthria%22">Dysarthria</searchLink><br />*<searchLink fieldCode="DE" term="%22Multilingualism%22">Multilingualism</searchLink><br />*<searchLink fieldCode="DE" term="%22Speech+evaluation%22">Speech evaluation</searchLink><br />*<searchLink fieldCode="DE" term="%22Speech+disorders%22">Speech disorders</searchLink><br />*<searchLink fieldCode="DE" term="%22Factor+analysis%22">Factor analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Vowels%22">Vowels</searchLink><br /><searchLink fieldCode="DE" term="%22Smartphones%22">Smartphones</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+regression+analysis%22">Multiple regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Fisher+exact+test%22">Fisher exact test</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Physiological+aspects+of+speech%22">Physiological aspects of speech</searchLink><br /><searchLink fieldCode="DE" term="%22Spanish+language%22">Spanish language</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+variance%22">Analysis of variance</searchLink><br /><searchLink fieldCode="DE" term="%22English+language%22">English language</searchLink><br /><searchLink fieldCode="DE" term="%22Huntington+disease%22">Huntington disease</searchLink><br /><searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Changes in voice and speech are characteristic symptoms of Huntington’s disease (HD). Objective methods for quantifying speech impairment that can be used across languages could facilitate assessment of disease progression and intervention strategies. The aim of this study was to analyze acoustic features to identify language-independent features that could be used to quantify speech dysfunction in English-, Spanish-, and Polish-speaking participants with HD. Method: Ninety participants with HD and 83 control participants performed sustained vowel, syllable repetition, and reading passage tasks recorded with previously validated methods using mobile devices. Language-independent features that differed between HD and controls were identified. Principal component analysis (PCA) and unsupervised clustering were applied to the language-independent features of the HD data set to identify subgroups within the HD data. Results: Forty-six language-independent acoustic features that were significantly different between control participants and participants with HD were identified. Following dimensionality reduction using PCA, four speech clusters were identified in the HD data set. Unified Huntington’s Disease Rating Scale (UHDRS) total motor score, total functional capacity, and composite UHDRS were significantly different for pairwise comparisons of subgroups. The percentage of HD participants with higher dysarthria score and disease stage also increased across clusters. Conclusion: The results support the application of acoustic features to objectively quantify speech impairment and disease severity in HD in multilanguage studies. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of American Journal of Speech-Language Pathology is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=177020414
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        Value: 10.1044/2024_AJSLP-23-00175
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        Text: English
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      – SubjectFull: Reading
        Type: general
      – SubjectFull: Dysarthria
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      – SubjectFull: Vowels
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      – SubjectFull: Research funding
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      – SubjectFull: Multiple regression analysis
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      – SubjectFull: Fisher exact test
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      – SubjectFull: Physiological aspects of speech
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      – SubjectFull: Spanish language
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      – SubjectFull: Analysis of variance
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      – SubjectFull: English language
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      – SubjectFull: Huntington disease
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      – SubjectFull: Biomarkers
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      – TitleFull: Language-Independent Acoustic Biomarkers for Quantifying Speech Impairment in Huntington’s Disease.
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              Text: May2024
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