  Children's speech recognition is considered a low-resource task mainly due to
the lack of publicly available data. There are several reasons for such data
scarcity, including expensive data collection and annotation processes, and
data privacy, among others. Transforming speech signals into discrete tokens
that do not carry sensitive information but capture both linguistic and
acoustic information could be a solution for privacy concerns. In this study,
we investigate the integration of discrete speech tokens into children's speech
recognition systems as input without significantly degrading the ASR
performance. Additionally, we explored single-view and multi-view strategies
for creating these discrete labels. Furthermore, we tested the models for
generalization capabilities with unseen domain and nativity dataset. Results
reveal that the discrete token ASR for children achieves nearly equivalent
performance with an approximate 83% reduction in parameters.
