If you use Ethos AT in your research, please cite it as:
@inproceedings{silva-etal-2026-software,
title = "Software for Automatic Speech Recognition via Whisper models applied to Oral History interviews in the {P}ortuguese language",
author = "Silva, Edgleide de Oliveira Clemente da and
Zagatti, Fernando Rezende and
Lopes, Filipe Loyola and
Duarte, Anderson Dias and
Bonacin, Rodrigo and
Alves, Angela Maria",
editor = "Souza, Marlo and
de-Dios-Flores, Iria and
Santos, Diana and
Freitas, Larissa and
Souza, Jackson Wilke da Cruz and
Ribeiro, Eug{\'e}nio",
booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-1.30/",
pages = "302--310",
ISBN = "979-8-89176-387-6",
abstract = "This paper presents Ethos AT, a desktop software for automatic transcription that uses OpenAI Whisper models, enabling local processing and ensuring data privacy and accessibility for users who are not necessarily programming experts, such as oral history researchers. A comparative analysis of six Whisper models (small, medium, large, large-v2, large-v3, and turbo) was conducted to analyze performance in terms of transcription accuracy, error types, and processing time. Results indicate that larger models achieve higher lexical accuracy, while smaller ones provide faster execution with acceptable quality for general use; the turbo model showed an effective balance between accuracy and speed. Overall, Ethos AT offers a secure, efficient, and user-friendly solution for academic and institutional contexts."
}