Phonetically rich corpus construction for a low-resourced language
Marcellus Amadeus William Alberto Cruz Castañeda Wilmer Lobato Niasche Aquino
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.