In our previous HMPL Curriculum 1 (HMPL-C1) study which focused on extending foreign language vocabulary for human learners and increase of speech-recognition accuracy of artificial learners, we have „observed increase in amount of matches between expected and predicted labels which was caused both by increase of human learner’s vocabulary, as well as by increase of recognition accuracy of machine’s speech-to-text model“ (Hromada & Kim, "Proof-of-concept of feasibility of human–machine peer learning for German noun vocabulary learning", Frontiers in Education, 2023)
in majority of reading exercises which are included in the Primer we already know the text in advance
we already know what utterances could be considered as correct lectures and which not
to every specific exercise - like vowel or syllable recognition – Primer associates a specific language model (scorer) which constraints the connectionist temporal classification (CTC) beam search to restricted amount of exercise-relevant answers
significantly constraining the search space of plausible solutions
INNOVATION 1 : implementation of exercise-specific scorers transforms a generic ASR problem (difficult) into multi-class classification problem (easier)