An experiment on an automated literature survey of data-driven speech enhancement methods
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916610843869184 |
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| author | Santos, Arthur dos Pereira, Jayr Nogueira, Rodrigo Masiero, Bruno Sander-Tavallaey, Shiva Zea, Elias |
| author_facet | Santos, Arthur dos Pereira, Jayr Nogueira, Rodrigo Masiero, Bruno Sander-Tavallaey, Shiva Zea, Elias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_06260 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An experiment on an automated literature survey of data-driven speech enhancement methods Santos, Arthur dos Pereira, Jayr Nogueira, Rodrigo Masiero, Bruno Sander-Tavallaey, Shiva Zea, Elias Sound Computation and Language Audio and Speech Processing 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. |
| title | An experiment on an automated literature survey of data-driven speech enhancement methods |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2310.06260 |