BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithm

TL;DR

This paper introduces BASPRO, a genetic algorithm-based system for automatically generating phonetically balanced Chinese speech scripts, improving speech processing model performance by providing more representative training data.

Contribution

BASPRO is the first system to automatically produce phonetically balanced Chinese speech scripts using genetic algorithms, enhancing speech corpus quality for model training.

Findings

The generated script covers 84% of real-world syllables.

The syllable distribution closely matches real-world data with 0.96 cosine similarity.

Models trained on the designed corpus outperform those trained on random data.

Abstract

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.