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 +1 more
10/10/2023
cs.SDcs.CLeess.AS

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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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Cite this paper

@article{santos2023experiment,
  title  = {An experiment on an automated literature survey of data-driven speech enhancement methods},
  author = {Arthur dos Santos and Jayr Pereira and Rodrigo Nogueira and Bruno Masiero and Shiva Sander-Tavallaey and Elias Zea},
  year   = {2023},
  eprint = {2310.06260},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.06260v1}
}

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