SAMPA: Transformer-based segmentation of prosodic boundaries in Brazilian Portuguese

Poster presented at the 16th Lisbon Machine Learning School (LxMLS 2026), Lisbon, Portugal. URL https://lxmls.it.pt/2026 This poster presents SAMPA, a Whisper-based approach for automatic prosodic boundary segmentation in Brazilian Portuguese (BP). Fine-tuned on the NURC-Sampa corpus and evaluated across both in-distribution and out-of-distribution (MuPe-Diversidades) datasets, SAMPA demonstrates that fine-tuning a speech recognition model to jointly transcribe and mark terminal prosodic boundaries outperforms standard baselines and traditional classifiers, advancing deep learning-based prosody modeling for BP.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-28
DOI
https://doi.org/10.5281/zenodo.23744158
Primary Topic
Speech Recognition and Synthesis
Type
article
Field-Weighted Citation Impact
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article

SAMPA: Transformer-based segmentation of prosodic boundaries in Brazilian Portuguese

Julio Cesar Galdino, Rodrigo de Souza Lima, Marcos Vinícius Treviso
1 links
Zenodo (CERN European Organization for Nuclear Research)
Speech Recognition and Synthesis
article

SAMPA: Transformer-based segmentation of prosodic boundaries in Brazilian Portuguese

Julio Cesar Galdino, Rodrigo de Souza Lima, Marcos Vinícius Treviso
article en

Abstract

Poster presented at the 16th Lisbon Machine Learning School (LxMLS 2026), Lisbon, Portugal. URL https://lxmls.it.pt/2026 This poster presents SAMPA, a Whisper-based approach for automatic prosodic boundary segmentation in Brazilian Portuguese (BP). Fine-tuned on the NURC-Sampa corpus and evaluated across both in-distribution and out-of-distribution (MuPe-Diversidades) datasets, SAMPA demonstrates that fine-tuning a speech recognition model to jointly transcribe and mark terminal prosodic boundaries outperforms standard baselines and traditional classifiers, advancing deep learning-based prosody modeling for BP.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 14%
Speech Recognition and Synthesis
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