A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders
A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders
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DOI：
10.1016/j.jvoice.2018.07.014
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发表时间：
2019-11-01
期刊：
影响因子：
2.2
通讯作者：
Dodderi, Thejaswi
中科院分区：
文献类型：
--
作者：
Hegde, Sarika;Shetty, Surendra;Dodderi, Thejaswi
人类发声系统是一种复杂的生物装置，能够调节音调和响度。固有的内部和/或外部因素通常会损害声带并导致声音发生一些变化。其后果反映在身体功能和情绪状态上。因此，尽早识别声音变化并为患者提供克服任何后果并提高生活质量的机会至关重要。在这一领域的工作中，使用机器学习技术自动检测语音障碍起着关键作用，因为它被证明有助于简化理解语音障碍的过程。近年来，许多研究人员研究了一种自动化系统技术，可以帮助临床医生早期诊断嗓音疾病。在本文中，我们对自动检测语音障碍的研究工作进行了调查，并探讨了如何识别不同类型的语音障碍。我们还分析了这些研究工作中使用的不同数据库、特征提取技术和机器学习方法。
The human voice production system is an intricate biological device capable of modulating pitch and loudness. Inherent internal and/or external factors often damage the vocal folds and result in some change of voice. The consequences are reflected in body functioning and emotional standing. Hence, it is paramount to identify voice changes at an early stage and provide the patient with an opportunity to overcome any ramification and enhance their quality of life. In this line of work, automatic detection of voice disorders using machine learning techniques plays a key role, as it is proven to help ease the process of understanding the voice disorder. In recent years, many researchers have investigated techniques for an automated system that helps clinicians with early diagnosis of voice disorders. In this paper, we present a survey of research work conducted on automatic detection of voice disorders and explore how it is able to identify the different types of voice disorders. We also analyze different databases, feature extraction techniques, and machine learning approaches used in these research works.