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A key desiderata for inclusive and accessible speech recognition technologyis ensuring its robust performance to children's speech. Notably, this includesthe rapidly advancing neural network based end-to-end speech recognitionsystems. Children speech recognition is more challenging due to the largerintra-inter speaker variability in terms of acoustic and linguisticcharacteristics compared to adult speech. Furthermore, the lack of adequate andappropriate children speech resources adds to the challenge of designing robustend-to-end neural architectures. This study provides a critical assessment ofautomatic children speech recognition through an empirical study ofcontemporary state-of-the-art end-to-end speech recognition systems. Insightsare provided on the aspects of training data requirements, adaptation onchildren data, and the effect of children age, utterance lengths, differentarchitectures and loss functions for end-to-end systems and role of languagemodels on the speech recognition performance.

End-to-End Neural Systems for Automatic Children Speech Recognition: An Empirical Study