Learner 1 (L1) - is a 5-year old – pre-school bilingual (90% German, 10% Slovak) daughter of the main author of this article

three HMPL-C2 exercise 1 (E1) sessions were executed on days 1, 3 and 5 of the study

each HMPL-C2-E1 session consisted of human-testing phase followed by a mutual human-machine learning phase

in each phase, sequences consisted of 5 repetitions of syllables started with occlusive labial consonant M or B and followed by the vowel A, E, I, O or U, thus yielding sequences from “MA MA MA MA MA” to “BU BU BU BU BU"

speech recordings collected during the learning phase subsequently provided input for the acoustic-model fine-tuning process

It is generally believed that acquisition of reading skill(s) can be fostered (resp. inhibited) by  learner’s exposure to appropriate (resp. inappropriate) social, pedagogic and instrumental context. It is also believed that well-designed digital tools may also help children learn how to read.

Primer is a post-smartphone, book-like, do-it-Yourself educational instrument (Bildunginstrument).

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%.