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.

中文翻译：

---

用于开发基于人工智能的帕金森病语音评估的合成数据生成技术（比较研究）

言语和语言的变化是帕金森病 (PD) 的最初症状之一。因此，多年来临床医生一直试图通过声音来识别帕金森病患者。由于人工智能 (AI) 的进步，医生可以利用基于人工智能的语音评估来发现帕金森病。这种人工智能系统可以使用机器学习分类器来开发，这些分类器是使用个人声音进行训练的。尽管一些研究已经显示出开发此类人工智能系统的合理结果，但这些系统需要更多的数据样本才能实现有希望的性能。本文探讨了使用基于深度学习的数据生成技术来提高作为此类系统核心的机器学习分类器的准确性。