The increasing number of scientific publications in acoustics, in general, presents difficulties in conducting traditional literature surveys. This work explores the use of a generative pre-trained transformer (GPT) model to automate a literature survey of 116 articles on data-driven speech enhancement methods. The main objective is to evaluate the capabilities and limitations of the model in providing accurate responses to specific queries about the papers selected from a reference human-based survey. While we see great potential to automate literature surveys in acoustics, improvements are needed to address technical questions more clearly and accurately.

翻译：声学领域科学出版物数量的普遍增长给传统文献综述带来了困难。本研究探索利用生成式预训练变换器（GPT）模型对116篇关于数据驱动语音增强方法的文献进行自动化综述。主要目标是在评估模型对源自人工参考综述所选论文的特定查询提供准确回答的能力与局限性。尽管我们看到了声学领域文献综述自动化的巨大潜力，但为更清晰准确地解答技术性问题仍需改进。