  Automatic Speech Recognition (ASR) systems are known to exhibit difficulties
when transcribing children's speech. This can mainly be attributed to the
absence of large children's speech corpora to train robust ASR models and the
resulting domain mismatch when decoding children's speech with systems trained
on adult data. In this paper, we propose multiple enhancements to alleviate
these issues. First, we propose a data augmentation technique based on the
source-filter model of speech to close the domain gap between adult and
children's speech. This enables us to leverage the data availability of adult
speech corpora by making these samples perceptually similar to children's
speech. Second, using this augmentation strategy, we apply transfer learning on
a Transformer model pre-trained on adult data. This model follows the recently
introduced XLS-R architecture, a wav2vec 2.0 model pre-trained on several
cross-lingual adult speech corpora to learn general and robust acoustic
frame-level representations. Adopting this model for the ASR task using adult
data augmented with the proposed source-filter warping strategy and a limited
amount of in-domain children's speech significantly outperforms previous
state-of-the-art results on the PF-STAR British English Children's Speech
corpus with a 4.86% WER on the official test set.
