  Automatic recognition systems for child speech are lagging behind those
dedicated to adult speech in the race of performance. This phenomenon is due to
the high acoustic and linguistic variability present in child speech caused by
their body development, as well as the lack of available child speech data.
Young readers speech additionally displays peculiarities, such as slow reading
rate and presence of reading mistakes, that hardens the task. This work
attempts to tackle the main challenges in phone acoustic modelling for young
child speech with limited data, and improve understanding of strengths and
weaknesses of a wide selection of model architectures in this domain. We find
that transfer learning techniques are highly efficient on end-to-end
architectures for adult-to-child adaptation with a small amount of child speech
data. Through transfer learning, a Transformer model complemented with a
Connectionist Temporal Classification (CTC) objective function, reaches a phone
error rate of 28.1%, outperforming a state-of-the-art DNN-HMM model by 6.6%
relative, as well as other end-to-end architectures by more than 8.5% relative.
An analysis of the models' performance on two specific reading tasks (isolated
words and sentences) is provided, showing the influence of the utterance length
on attention-based and CTC-based models. The Transformer+CTC model displays an
ability to better detect reading mistakes made by children, that can be
attributed to the CTC objective function effectively constraining the attention
mechanisms to be monotonic.
