@inproceedings{araujo-etal-2026-certas,
    title = "Certas Palavras: A 1980s-90s {B}razilian Radio Corpus to Test {TTS} Models in Noisy Multi-Speaker Dialogues",
    author = "Ara{\'u}jo, Gustavo Evangelista  and
      Ponti, Moacir  and
      Junior, Arnaldo Candido  and
      Leal, Sidney  and
      Casanova, Edresson  and
      Silva, Renato Moraes  and
      Jr., Miguel Oliveira  and
      Santos, Adriana Barbosa  and
      Lopes, Gustavo Wadas  and
      Aluisio, Sandra",
    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://preview.aclanthology.org/ingest-dnd/2026.propor-1.81/",
    pages = "819--829",
    ISBN = "979-8-89176-387-6",
    abstract = "Robust text-to-speech (TTS) systems must be trained on speech that mirrors the variability and imperfections of spontaneous dialogues. However, TTS systems trained on existing Brazilian Portuguese datasets are typically limited to clean, scripted, or studio-recorded speech. Certas Palavras (CP) bridges this gap with 70 hours of spontaneous, multi-speaker dialogues from a Brazilian radio program broadcast in the 1980s{--}1990s. The extensive manual annotation process captures conversational dynamics, including orality markers, filled pauses, and hesitations. For the analog medium, we annotated non-verbal phenomena as musical interference, noise and segmental corrections, describing a challenging acoustic environment for synthesis. Baseline YourTTS and F5-TTS models were trained in a 9-hour subset featuring one of the two main hosts of Certas Palavras. Baseline YourTTS and F5-TTS models were trained on a 9-hour single-speaker subset corresponding to one of the main program hosts. Objective evaluation shows that the synthesized speech remains intelligible, with moderate WER and CER. In contrast, subjective evaluation reveals a clear gap in perceived naturalness, with lower MOS scores and higher inter-rater variability compared to ground-truth audio. Together, these properties make the dataset a strong benchmark for TTS robustness."
}