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 \textit{corpus}.
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
\textit{corpus} 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.

本研究介绍了一种用于创建巴西葡萄牙语语音覆盖度广泛的语音语料库的新方法，并提出了基于声音 - 口型语音特征的新的音素分类方法，通过使用该算法，我们使得与目前可用的语音丰富语料库相比，一种样本中不同音素的百分比增加了 55.8％。