  Automatic Speech Recognition (ASR) systems have progressed significantly in
their performance on adult speech data; however, transcribing child speech
remains challenging due to the acoustic differences in the characteristics of
child and adult voices. This work aims to explore the potential of adapting
state-of-the-art Conformer-transducer models to child speech to improve child
speech recognition performance. Furthermore, the results are compared with
those of self-supervised wav2vec2 models and semi-supervised multi-domain
Whisper models that were previously finetuned on the same data. We demonstrate
that finetuning Conformer-transducer models on child speech yields significant
improvements in ASR performance on child speech, compared to the non-finetuned
models. We also show Whisper and wav2vec2 adaptation on different child speech
datasets. Our detailed comparative analysis shows that wav2vec2 provides the
most consistent performance improvements among the three methods studied.
