Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review
Authors: Brahmi, Z., Mahyoob, M., Al-Sarem, M., Algaraady, J., Bousselmi, K. and Alblwi, A.
Journal: Psychology Research and Behavior Management
Volume: 17
Pages: 2205-2232
eISSN: 1179-1578
DOI: 10.2147/PRBM.S460283
Abstract:Purpose: Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies. Methods: The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms. Originality: This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014–2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse. Findings: The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).
Source: Scopus
Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review.
Authors: Brahmi, Z., Mahyoob, M., Al-Sarem, M., Algaraady, J., Bousselmi, K. and Alblwi, A.
Journal: Psychol Res Behav Manag
Volume: 17
Pages: 2205-2232
ISSN: 1179-1578
DOI: 10.2147/PRBM.S460283
Abstract:PURPOSE: Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies. METHODS: The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms. ORIGINALITY: This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014-2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse. FINDINGS: The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).
Source: PubMed
Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review
Authors: Brahmi, Z., Mahyoob, M., Al-Sarem, M., Algaraady, J., Bousselmi, K. and Alblwi, A.
Journal: PSYCHOLOGY RESEARCH AND BEHAVIOR MANAGEMENT
Volume: 17
Pages: 2205-2232
ISSN: 1179-1578
DOI: 10.2147/PRBM.S460283
Source: Web of Science (Lite)
Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review.
Authors: Brahmi, Z., Mahyoob, M., Al-Sarem, M., Algaraady, J., Bousselmi, K. and Alblwi, A.
Journal: Psychology research and behavior management
Volume: 17
Pages: 2205-2232
eISSN: 1179-1578
ISSN: 1179-1578
DOI: 10.2147/prbm.s460283
Abstract:Purpose
Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies.Methods
The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms.Originality
This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014-2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse.Findings
The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).Source: Europe PubMed Central