  Automatic Speech Recognition (ASR) systems often struggle with transcribing
child speech due to the lack of large child speech datasets required to
accurately train child-friendly ASR models. However, there are huge amounts of
annotated adult speech datasets which were used to create multilingual ASR
models, such as Whisper. Our work aims to explore whether such models can be
adapted to child speech to improve ASR for children. In addition, we compare
Whisper child-adaptations with finetuned self-supervised models, such as
wav2vec2. We demonstrate that finetuning Whisper on child speech yields
significant improvements in ASR performance on child speech, compared to non
finetuned Whisper models. Additionally, utilizing self-supervised Wav2vec2
models that have been finetuned on child speech outperforms Whisper finetuning.
