Digital Primer Implementation of Human-Machine Peer Learning for Reading Acquisition: Introducing Curriculum 2

Abstract

The aim of the digital primer project is cognitive enrichment and fostering of acquisition of basic literacy and numeracy of 5 – 10 year old children. Here, we focus on Primer's ability to accurately process child speech which is fundamental to the acquisition of reading component of the Primer. We first note that automatic speech recognition (ASR) and speech-to-text of child speech is a challenging task even for large-scale, cloud-based ASR systems. Given that the Primer is an embedded AI artefact which aims to perform all computations on edge devices like RaspberryPi or Nvidia Jetson, the task is even more challenging and special tricks and hacks need to be implemented to execute all necessary inferences in quasi-real-time. One such trick explored in this article is transformation of a generic ASR problem into much more constrained multiclass-classification problem by means of task-specific language models / scorers. Another one relates to adoption of "human machine peer learning" (HMPL) strategy whereby the DeepSpeech model behind the ASR system is supposed to gradually adapt its parameters to particular characteristics of the child using it. In this article, we describe first, syllable-oriented exercise by means of which the Primer aimed to assist one 5-year-old pre-schooler in increase of her reading competence. The pupil went through sequence of exercises composed of evaluation and learning tasks. Consistently with previous HMPL study, we observe increase of both child's reading skill as well as of machine's ability to accurately process child's speech.

Keywords: digital primer, reading acquisition, human-machine peer learning, speech-to-text, speech command classification, voice menu, speaker identification

DOI: 10.54941/ahfe1004027