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.
@article{arxiv.2310.06260,
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},
journal= {arXiv preprint arXiv:2310.06260},
year = {2025}
}