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arXiv · 2310.06260

An experiment on an automated literature survey of data-driven speech enhancement methods

Arthur dos Santos·Jayr Pereira·Rodrigo Nogueira·Bruno Masiero·Shiva Sander-Tavallaey·Elias Zea
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Abstract

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.

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Arthur dos Santos, Jayr Pereira, Rodrigo Nogueira, Bruno Masiero, Shiva Sander-Tavallaey, Elias Zea. 2023-10-10. An experiment on an automated literature survey of data-driven speech enhancement methods. https://doi.org/10.1051/aacus%2F2023067

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