Centre for Neuroscience of Speech, The University of Melbourne, Victoria, Australia.
Department of Audiology and Speech Pathology, The University of Melbourne, Victoria, Australia.
J Speech Lang Hear Res. 2022 Sep 12;65(9):3239-3263. doi: 10.1044/2022_JSLHR-21-00647. Epub 2022 Aug 31.
The human voice changes with the progression of neurological disease and the onset of diseases that affect articulators, often decreasing the effectiveness of communication. These changes can be objectively measured using signal processing techniques that extract acoustic features. When measuring acoustic features, there are often several steps and assumptions that might be known to experts in acoustics and phonetics, but are less transparent for other disciplines (e.g., clinical medicine, speech pathology, engineering, and data science). This tutorial describes these signal processing techniques, explicitly outlines the underlying steps for accurate measurement, and discusses the implications of clinical acoustic markers.
We establish a vocabulary using straightforward terms, provide visualizations to achieve common ground, and guide understanding for those outside the domains of acoustics and auditory signal processing. Where possible, we highlight the best practices for measuring clinical acoustic markers and suggest resources for obtaining and further understanding these measures.
随着神经疾病的进展和影响发音器官的疾病的发生，人的声音会发生变化，这常常会降低沟通的有效性。这些变化可以使用提取声学特征的信号处理技术来客观地测量。在测量声学特征时，通常有几个步骤和假设，这些对于声学和语音学专家来说是已知的，但对于其他学科（例如临床医学、言语病理学、工程学和数据科学）来说则不太透明。本教程介绍了这些信号处理技术，明确概述了准确测量的基础步骤，并讨论了临床声学标记的意义。
我们使用通俗易懂的术语建立词汇，提供可视化效果以达成共识，并为那些不属于声学和听觉信号处理领域的人提供指导。在可能的情况下，我们强调了测量临床声学标记的最佳实践，并为获取和进一步理解这些测量方法提供了资源。
