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

• URL: http://arxiv.org/abs/2310.06260v1
• Date: Tue, 10 Oct 2023 02:07:24 GMT
• Title: An experiment on an automated literature survey of data-driven speech enhancement methods
• Authors: Arthur dos Santos, Jayr Pereira, Rodrigo Nogueira, Bruno Masiero, Shiva Sander-Tavallaey, Elias Zea
• Abstract summary: 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.
• Score: 5.931978628000179
• License: http://creativecommons.org/licenses/by/4.0/
• 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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