  The performance of speech-processing models is heavily influenced by the
speech corpus that is used for training and evaluation. In this study, we
propose BAlanced Script PROducer (BASPRO) system, which can automatically
construct a phonetically balanced and rich set of Chinese sentences for
collecting Mandarin Chinese speech data. First, we used pretrained natural
language processing systems to extract ten-character candidate sentences from a
large corpus of Chinese news texts. Then, we applied a genetic algorithm-based
method to select 20 phonetically balanced sentence sets, each containing 20
sentences, from the candidate sentences. Using BASPRO, we obtained a recording
script called TMNews, which contains 400 ten-character sentences. TMNews covers
84% of the syllables used in the real world. Moreover, the syllable
distribution has 0.96 cosine similarity to the real-world syllable
distribution. We converted the script into a speech corpus using two
text-to-speech systems. Using the designed speech corpus, we tested the
performances of speech enhancement (SE) and automatic speech recognition (ASR),
which are one of the most important regression- and classification-based speech
processing tasks, respectively. The experimental results show that the SE and
ASR models trained on the designed speech corpus outperform their counterparts
trained on a randomly composed speech corpus.
