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

IF 2.8 3区心理学 Q2 PSYCHOLOGY, CLINICAL

Psychology Research and Behavior Management Pub Date : 2024-05-31 DOI:10.2147/prbm.s460283

Zaki Brahmi, Mohammad Mahyoob, Mohammed Al-Sarem, Jeehaan Algaraady, Khadija Bousselmi, Abdulaziz Alblwi

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引用次数: 0

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).

Keywords: speech disorder, speech recognition, dysarthria, machine learning, assistive technologies

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探索机器学习在诊断和治疗语言障碍中的作用：系统性文献综述

目的：语言障碍会妨碍社交活动，阻碍有效沟通，从而严重影响整体生活质量。本研究针对基于机器学习的语音障碍辅助技术的系统性综述方面存在的空白进行了研究。首要目的是通过系统性文献综述（SLR）对该领域进行全面概述，并为基于机器学习的解决方案和相关研究提供有价值的见解：本研究采用了系统方法，使用了系统文献综述（SLR）方法。本研究广泛考察了有关基于机器学习的语音障碍辅助技术的现有文献。研究特别关注了机器学习技术、训练阶段所使用数据集的特点、说话者语言、特征提取技术以及机器学习算法所使用的特征：本研究通过系统地探索语言障碍辅助技术中的机器学习领域，为现有文献做出了贡献。其独创性在于对十年内（2014-2023 年）语言障碍用户的 ML 语音识别进行了重点调查。该研究强调与机器学习技术、数据集特征、语言、特征提取技术和特征集相关的系统性研究问题，为当前的讨论增添了独特而全面的视角：系统性文献综述确定了 2014 年至 2023 年间发表的重要趋势和关键研究。在对来自著名期刊的 65 篇论文的分析中，支持向量机和神经网络（CNN、DNN）是使用最多的 ML 技术（20%，16.92%），研究最多的疾病是构音障碍（35/65，54% 的研究）。此外，2018 年后，基于神经网络的架构（主要是 CNN 和 DNN）的使用量激增。几乎一半的纳入研究发表于 2021 年至 2022 年之间）。关键词：语言障碍、语音识别、构音障碍、机器学习、辅助技术

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来源期刊

Psychology Research and Behavior Management Multiple-

CiteScore

4.50

自引率

4.70%

发文量

341

审稿时长

16 weeks

期刊介绍： Psychology Research and Behavior Management is an international, peer-reviewed, open access journal focusing on the science of psychology and its application in behavior management to develop improved outcomes in the clinical, educational, sports and business arenas. Specific topics covered in the journal include: -Neuroscience, memory and decision making -Behavior modification and management -Clinical applications -Business and sports performance management -Social and developmental studies -Animal studies The journal welcomes submitted papers covering original research, clinical studies, surveys, reviews and evaluations, guidelines, expert opinion and commentary, case reports and extended reports.

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