Voice Analysis for Neurological Disorder Recognition-A Systematic Review and Perspective on Emerging Trends.
Voice Analysis for Neurological Disorder Recognition-A Systematic Review and Perspective on Emerging Trends.
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DOI：
10.3389/fdgth.2022.842301
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发表时间：
2022
影响因子：
--
通讯作者：
Arnrich, Bert
中科院分区：
文献类型：
--
作者：
Hecker, Pascal;Steckhan, Nico;Eyben, Florian;Schuller, Bjoern W.;Arnrich, Bert
关键词：
从声音中量化神经系统疾病是一个快速发展的研究领域，并有望实现不引人注目的大规模疾病监测。数据记录设置和数据分析管道都是有效获取参与者相关信息的关键方面。因此，我们进行了一项系统性综述，以提供各种神经系统疾病实践的高水平概述，并强调新出现的趋势。通过PubMed、Web of Science和IEEE Xplore进行基于PRISMA的文献检索，以识别其中包含原创（即，新记录的数据集。关注的疾病是精神疾病以及神经退行性疾病，如双相情感障碍、抑郁症和应激，以及肌萎缩性侧索硬化症、阿尔茨海默病和帕金森病，以及言语障碍（失语症、构音障碍和发音困难）。在检索到的43项研究中，帕金森病最突出的代表是19个发现的数据集。自由演讲和阅读演讲任务最常用于各种疾病。除了流行的特征提取工具包，许多研究利用定制的特征集。声学特征与精神和神经退行性疾病的相关性。在分析方面，通常使用单个特征的显著性的统计分析，以及预测建模方法，特别是使用支持向量机和少量人工神经网络。未来研究的一个新兴趋势和建议是收集日常生活中的数据，以促进纵向数据收集，并更自然地捕捉参与者的行为。另一个新出现的趋势是记录更多的语音模式，这可能会提高分析性能。
Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.
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