  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. They were able to demonstrate some improvement on a limited
testset. This paper builds on these findings by enhancing the utility of the
MyST dataset through more efficient data preprocessing. We reduce the Word
Error Rate (WER) on the MyST testset 13.93% to 9.11% with Whisper-Small and
from 13.23% to 8.61% with Whisper-Medium and show that this improvement can be
generalized to unseen datasets. 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.
