Acta Acustica (Jan 2024)

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

  • dos Santos Arthur,
  • Pereira Jayr,
  • Nogueira Rodrigo,
  • Masiero Bruno,
  • Tavallaey Shiva Sander,
  • Zea Elias

Journal volume & issue
Vol. 8
p. 2


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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 117 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.