Speech and phonological impairment across Alzheimer's disease severity

Luchesi, Maysa; Ortiz, Karin Zazo; Bertolucci, Paulo Henrique Ferreira; Tsujimoto, Tamy; Minett, Thaı́s

doi:10.1016/j.jcomdis.2023.106364

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…In addition, the study focuses on the ML role in treating these disorders, considering their potential causes, either biological, psychological, or environmental, regardless of the presence of cognitive impairments such as Down’s syndrome 37 or Alzheimer’s disease. 38 , 39 This work aims to comprehensively analyze the ML techniques for speech impairment recognition, focusing on the challenges and limitations. The primary contribution of our work implies: Providing a review highlighting the existing ML methods, algorithms, features extraction techniques, models’ performance metrics, and the characteristics of the obtained datasets, focusing on discovering the state-of-The-art of all these techniques utilized by scholars in the field.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

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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).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

View full text Add to dashboard Cite

show abstract

Review of voice biomarkers in the screening of neurodegenerative diseases

Chen,

Li,

Han

et al. 2024

Interdisciplinary Nursing Research

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Neurodegenerative diseases significantly impact patients and their families, making early identification crucial for improving patients’ quality of life and reducing care burdens. Current screening methods for neurodegenerative diseases, such as dementia and mild cognitive impairment, still rely on subjective assessments or expensive techniques like invasive cerebrospinal fluid analysis and magnetic resonance imaging. These factors make early identification challenging. Voice biomarkers present a promising alternative as convenient, non-invasive, and low-cost screening tools. With the application and development of artificial intelligence and big data, the prediction and screening of neurodegenerative diseases based on voice data have become a research focus. This article reviews the progress in voice biomarkers for neurodegenerative diseases screening and classification. It summarizes relevant studies on both single and multimodal data, identifies existing challenges, and suggests future research directions to enhance the application of voice biomarkers in neurodegenerative disease contexts.

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Articulatory precision from connected speech as a marker of cognitive decline in Alzheimer’s disease risk-enriched cohorts

Xu,

Chen,

Mueller

et al. 2024

Journal of Alzheimer’s Disease

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Background Mild cognitive impairment (MCI) has been recognized as a possible precursor to Alzheimer’s disease (AD). Recent research focusing on connected speech has uncovered various features strongly correlated with MCI due to AD and related dementias. Despite these advancements, the impact of early cognitive decline on articulatory precision has not been thoroughly investigated. Objective This study introduced the phoneme log-likelihood ratio (PLLR) to assess the articulatory precision of speakers across different cognitive status levels and compared its effectiveness to existing well-studied acoustic features. Methods The study utilized speech recordings from a picture description task, which included data from 324 cognitively unimpaired participants with low amyloid-β burden (CU, Aβ([Formula: see text])), 47 cognitively unimpaired participants with high amyloid-β burden (CU, Aβ([Formula: see text])), 69 participants with MCI, and 20 participants with dementia. Nine acoustic features were extracted from the speech recordings, covering three categories: speech fluency, speech pace, and articulatory precision. Welch’s [Formula: see text]-test and Hedge’s [Formula: see text] were adopted to assess their discriminative ability. Results A reduction in speech fluency and pace was observed among participants in the MCI and dementia groups. The PLLR shows large effect sizes in distinguishing between cognitively unimpaired participants with low Aβ burden and those diagnosed with MCI or dementia. Additionally, the test-retest reliability experiment indicated moderate repeatability of the features under study. Conclusions The study reveals PLLR as a sensitive indicator capable of detecting subtle articulatory variations across groups, while also providing further support for the association between reduced articulatory precision and early cognitive decline.

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Speech and phonological impairment across Alzheimer's disease severity

Cited by 4 publications

References 32 publications

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Review of voice biomarkers in the screening of neurodegenerative diseases

Articulatory precision from connected speech as a marker of cognitive decline in Alzheimer’s disease risk-enriched cohorts

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