# Phonetically rich corpus construction for a low-resourced language

arXiv: https://arxiv.org/abs/2402.05794 · HTML: https://mlsanity.com/paper/2402.05794/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model](https://mlsanity.com/paper/2104.05557/) (2021)
- [TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese](https://mlsanity.com/paper/2005.05144/) (2020)
- [CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian Portuguese](https://mlsanity.com/paper/2110.15731/) (2021)

## Abstract (arXiv)

Speech technologies rely on capturing a speaker's voice variability while obtaining comprehensive language information. Textual prompts and sentence selection methods have been proposed in the literature to comprise such adequate phonetic data, referred to as a phonetically rich textitcorpus. However, they are still insufficient for acoustic modeling, especially critical for languages with limited resources. Hence, this paper proposes a novel approach and outlines the methodological aspects required to create a textitcorpus with broad phonetic coverage for a low-resourced language, Brazilian Portuguese. Our methodology includes text dataset collection up to a sentence selection algorithm based on triphone distribution. Furthermore, we propose a new phonemic classification according to acoustic-articulatory speech features since the absolute number of distinct triphones, or low-probability triphones, does not guarantee an adequate representation of every possible combination. Using our algorithm, we achieve a 55.8% higher percentage of distinct triphones -- for samples of similar size -- while the currently available phonetic-rich corpus, CETUC and TTS-Portuguese, 12.6% and 12.3% in comparison to a non-phonetically rich dataset.