Recent advancements in Automatic Speech Recognition (ASR) systems,
exemplified by Whisper, have demonstrated the potential of these systems to
approach human-level performance given sufficient data. However, this progress
doesn't readily extend to ASR for children due to the limited availability of
suitable child-specific databases and the distinct characteristics of
children's speech. A recent study investigated leveraging the My Science Tutor
(MyST) children's speech corpus to enhance Whisper's performance in recognizing
children's speech. This paper builds on these findings by enhancing the utility
of the MyST dataset through more efficient data preprocessing. We also
highlight important challenges towards improving children's ASR performance.
The results showcase the viable and efficient integration of Whisper for
effective children's speech recognition.

近期在自动语音识别系统（ASR）方面的最新进展，如 Whisper，已经显示出这些系统在充足的数据支持下能够接近人类水平的性能。然而，由于适合儿童的特定数据库的有限性以及儿童语音的独特特征，这一进展并不容易延伸到儿童 ASR 领域。最近的一项研究调查了利用 My Science Tutor (MyST) 儿童语音语料库提高 Whisper 在儿童语音识别方面的性能。本文在此基础上通过更高效的数据预处理提升了 MyST 数据集的实用性，同时还强调了改善儿童 ASR 性能的重要挑战。结果展示了将 Whisper 有效集成到改善儿童语音识别领域的可行性和高效性。