■言语障碍通过阻碍社会运作和阻碍有效沟通而深刻影响整体生活质量。这项研究解决了针对言语障碍患者的基于机器学习的辅助技术的系统评价中的差距。首要目的是通过系统文献综述(SLR)提供该领域的全面概述,并为基于ML的解决方案和相关研究提供有价值的见解。
■这项研究采用了系统的方法,利用系统文献综述(SLR)方法。该研究广泛考察了基于机器学习的语音障碍辅助技术的现有文献。特别关注ML技术,训练阶段被利用数据集的特征,说话者语言,特征提取技术,以及ML算法采用的功能。
■这项研究通过系统地探索语音障碍辅助技术中的机器学习景观,为现有文献做出了贡献。独创性在于对超过十年(2014-2023年)的受损语音障碍用户的ML语音识别进行了重点调查。强调与ML技术相关的系统研究问题,数据集特征,语言,特征提取技术,和功能集为当前的话语增添了独特而全面的视角。
■系统文献综述确定了2014年至2023年之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)是最常用的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,使用基于神经网络的架构的热潮,主要是CNN和DNN,是在2018年之后观察到的。几乎一半的纳入研究是在2021年至2022年之间发表的)。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).