@inproceedings{evaldo-leal-etal-2025-mupe,
    title = "{M}u{P}e Life Stories Dataset: Spontaneous Speech in {B}razilian {P}ortuguese with a Case Study Evaluation on {ASR} Bias against Speakers Groups and Topic Modeling",
    author = "Evaldo Leal, Sidney  and
      Candido Junior, Arnaldo  and
      Marcacini, Ricardo  and
      Casanova, Edresson  and
      Gon{\c{c}}alves, Odilon  and
      Silva Soares, Anderson  and
      Freitas Lima, Rodrigo  and
      Stefanel Gris, Lucas Rafael  and
      Alu{\'i}sio, Sandra",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.407/",
    pages = "6076--6087",
    abstract = "Recently, several public datasets for automatic speech recognition (ASR) in Brazilian Portuguese (BP) have been released, improving ASR systems performance. However, these datasets lack diversity in terms of age groups, regional accents, and education levels. In this paper, we present a new publicly available dataset consisting of 289 life story interviews (365 hours), featuring a broad range of speakers varying in age, education, and regional accents. First, we demonstrated the presence of bias in current BP ASR models concerning education levels and age groups. Second, we showed that our dataset helps mitigate these biases. Additionally, an ASR model trained on our dataset performed better during evaluation on a diverse test set. Finally, the ASR model trained with our dataset was extrinsically evaluated through a topic modeling task that utilized the automatically transcribed output."
}