@article{udawatta-etal-2026-ttsvowelviz,
    title = "{TTSV}owel{V}iz: A Tool for Visualising Text-to-Speech Model Training via Vowel Spaces",
    author = "Udawatta, Pasindu  and
      James, Jesin  and
      B T, Balamurali  and
      Watson, Catherine Inez  and
      Nicholas, Ake  and
      Abeysinghe, Binu Nisal",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.375/",
    pages = "4778--4786",
    abstract = "In text-to-speech (TTS) model training, the saturation of the loss curve indicates how well a model learns the characteristics of the training dataset. But it does not reveal the linguistic properties learned by the model. Existing TTS approaches miss the potential to incorporate linguistic insights into model training. We introduce TTSVowelViz, a novel tool that visualises static and dynamic vowel spaces during model training, bridging linguistic knowledge and TTS model development. It helps identify which vowel sounds are accurately learned and how the vowel spaces are evolved during training. To assess TTSVowelViz, we fine-tuned a TTS model from General American English to New Zealand English and conducted a perception test. Our results show that the formants of specific vowels in the vowel spaces generated by TTSVowelViz align with human perception, effectively visualising the perceived accent shift. This work highlights vowel space visualisation as a valuable interpretability tool for TTS training."
}