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Recent advancements in Automatic Speech Recognition (ASR) systems,exemplified by Whisper, have demonstrated the potential of these systems toapproach human-level performance given sufficient data. However, this progressdoesn't readily extend to ASR for children due to the limited availability ofsuitable child-specific databases and the distinct characteristics ofchildren's speech. A recent study investigated leveraging the My Science Tutor(MyST) children's speech corpus to enhance Whisper's performance in recognizingchildren's speech. They were able to demonstrate some improvement on a limitedtestset. This paper builds on these findings by enhancing the utility of theMyST dataset through more efficient data preprocessing. We reduce the WordError Rate (WER) on the MyST testset 13.93% to 9.11% with Whisper-Small andfrom 13.23% to 8.61% with Whisper-Medium and show that this improvement can begeneralized to unseen datasets. We also highlight important challenges towardsimproving children's ASR performance. The results showcase the viable andefficient integration of Whisper for effective children's speech recognition.

Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults