Changes in speech and language are among the first signs of Parkinson's disease (PD). Thus, clinicians have tried to identify individuals with PD from their voices for years. Doctors can leverage AI-based speech assessments to spot PD thanks to advancements in artificial intelligence (AI). Such AI systems can be developed using machine learning classifiers that have been trained using individuals' voices. Although several studies have shown reasonable results in developing such AI systems, these systems would need more data samples to achieve promising performance. This paper explores using deep learning-based data generation techniques on the accuracy of machine learning classifiers that are the core of such systems.

翻译：言语和语言的变化是帕金森病（PD）的早期征兆之一。因此，临床医生多年来一直试图通过患者的嗓音识别帕金森病。得益于人工智能（AI）的进步，医生可以借助基于AI的语音评估来发现帕金森病。这类AI系统可通过使用个体嗓音训练的机器学习分类器来开发。尽管多项研究在开发此类AI系统方面取得了合理成果，但为达到理想性能，这些系统仍需要更多数据样本。本文探讨了基于深度学习的生成技术对此类系统核心——机器学习分类器准确性的影响。