  A key desiderata for inclusive and accessible speech recognition technology
is ensuring its robust performance to children's speech. Notably, this includes
the rapidly advancing neural network based end-to-end speech recognition
systems. Children speech recognition is more challenging due to the larger
intra-inter speaker variability in terms of acoustic and linguistic
characteristics compared to adult speech. Furthermore, the lack of adequate and
appropriate children speech resources adds to the challenge of designing robust
end-to-end neural architectures. This study provides a critical assessment of
automatic children speech recognition through an empirical study of
contemporary state-of-the-art end-to-end speech recognition systems. Insights
are provided on the aspects of training data requirements, adaptation on
children data, and the effect of children age, utterance lengths, different
architectures and loss functions for end-to-end systems and role of language
models on the speech recognition performance.
