As of 2023, there exists no publicly available ASR model which could accurately and reliably process child speech.

In our IHIET 2023 article, we introduce two innovations with which the problem can be partially bypasssed in context of digitally supported reading acquisition app:

1. Transformation of a generic ASR problem into a sort of extended multi-class classification problem by means of extending a generic acoustic model with a domain-specific, minimalist language model (“scorer”).
2. Human-machine peer learning (HMPL) whereby the artificial utterence-processing tutor U incrementally and gradually adapts its parameters to a particular learner, a human individual I.

In concrete terms, we have shown that after three sessions focusing on acquisition of grapheme-vowel and CV-bigrapheme correspondences had lead, in case of one particular learner, to decrease of WER from 96% to 48%.