The Detection of Dysarthria Severity Levels Using AI Models: A Review
<p dir="ltr">Dysarthria, a speech disorder stemming from neurological conditions, affects communication and life quality. Precise classification and severity assessment are pivotal for therapy but are often subjective in traditional speech-language pathologist evaluations. Machine le...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2024
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| الموضوعات: | |
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| _version_ | 1864513543112491008 |
|---|---|
| author | Afnan Al-Ali (16888695) |
| author2 | Somaya Al-Maadeed (5178131) Moutaz Saleh (14151402) Rani Chinnappa Naidu (21805739) Zachariah C. Alex (21805742) Prakash Ramachandran (3801025) Rajeev Khoodeeram (21805745) Rajesh Kumar M (5865578) |
| author2_role | author author author author author author author |
| author_facet | Afnan Al-Ali (16888695) Somaya Al-Maadeed (5178131) Moutaz Saleh (14151402) Rani Chinnappa Naidu (21805739) Zachariah C. Alex (21805742) Prakash Ramachandran (3801025) Rajeev Khoodeeram (21805745) Rajesh Kumar M (5865578) |
| author_role | author |
| dc.creator.none.fl_str_mv | Afnan Al-Ali (16888695) Somaya Al-Maadeed (5178131) Moutaz Saleh (14151402) Rani Chinnappa Naidu (21805739) Zachariah C. Alex (21805742) Prakash Ramachandran (3801025) Rajeev Khoodeeram (21805745) Rajesh Kumar M (5865578) |
| dc.date.none.fl_str_mv | 2024-03-28T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3382574 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/The_Detection_of_Dysarthria_Severity_Levels_Using_AI_Models_A_Review/29665427 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Health sciences Health services and systems Dysarthria classification severity levels artificial intelligence (AI)-based models intelligibility Feature extraction Speech processing Lips Spectrogram Medical services Neurological diseases Artificial intelligence Classification algorithms Speech analysis |
| dc.title.none.fl_str_mv | The Detection of Dysarthria Severity Levels Using AI Models: A Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Dysarthria, a speech disorder stemming from neurological conditions, affects communication and life quality. Precise classification and severity assessment are pivotal for therapy but are often subjective in traditional speech-language pathologist evaluations. Machine learning models offer objective assessment potential, enhancing diagnostic precision. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels, highlighting effective features for automatic classification and optimal AI techniques. We systematically reviewed the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3382574" target="_blank">https://dx.doi.org/10.1109/access.2024.3382574</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7af21bf148d5d63cb49d13d697effb36 |
| identifier_str_mv | 10.1109/access.2024.3382574 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29665427 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The Detection of Dysarthria Severity Levels Using AI Models: A ReviewAfnan Al-Ali (16888695)Somaya Al-Maadeed (5178131)Moutaz Saleh (14151402)Rani Chinnappa Naidu (21805739)Zachariah C. Alex (21805742)Prakash Ramachandran (3801025)Rajeev Khoodeeram (21805745)Rajesh Kumar M (5865578)EngineeringBiomedical engineeringHealth sciencesHealth services and systemsDysarthriaclassificationseverity levelsartificial intelligence (AI)-based modelsintelligibilityFeature extractionSpeech processingLipsSpectrogramMedical servicesNeurological diseasesArtificial intelligenceClassification algorithmsSpeech analysis<p dir="ltr">Dysarthria, a speech disorder stemming from neurological conditions, affects communication and life quality. Precise classification and severity assessment are pivotal for therapy but are often subjective in traditional speech-language pathologist evaluations. Machine learning models offer objective assessment potential, enhancing diagnostic precision. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels, highlighting effective features for automatic classification and optimal AI techniques. We systematically reviewed the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3382574" target="_blank">https://dx.doi.org/10.1109/access.2024.3382574</a></p>2024-03-28T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3382574https://figshare.com/articles/journal_contribution/The_Detection_of_Dysarthria_Severity_Levels_Using_AI_Models_A_Review/29665427CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296654272024-03-28T12:00:00Z |
| spellingShingle | The Detection of Dysarthria Severity Levels Using AI Models: A Review Afnan Al-Ali (16888695) Engineering Biomedical engineering Health sciences Health services and systems Dysarthria classification severity levels artificial intelligence (AI)-based models intelligibility Feature extraction Speech processing Lips Spectrogram Medical services Neurological diseases Artificial intelligence Classification algorithms Speech analysis |
| status_str | publishedVersion |
| title | The Detection of Dysarthria Severity Levels Using AI Models: A Review |
| title_full | The Detection of Dysarthria Severity Levels Using AI Models: A Review |
| title_fullStr | The Detection of Dysarthria Severity Levels Using AI Models: A Review |
| title_full_unstemmed | The Detection of Dysarthria Severity Levels Using AI Models: A Review |
| title_short | The Detection of Dysarthria Severity Levels Using AI Models: A Review |
| title_sort | The Detection of Dysarthria Severity Levels Using AI Models: A Review |
| topic | Engineering Biomedical engineering Health sciences Health services and systems Dysarthria classification severity levels artificial intelligence (AI)-based models intelligibility Feature extraction Speech processing Lips Spectrogram Medical services Neurological diseases Artificial intelligence Classification algorithms Speech analysis |