Natural Language Processing has massively evolved during the last years and many up-to-date applications integrate different speech tools in order to create an enhanced user experiences. For obtaining a seamless integration of existing speech recognition systems, there is a trending interest for developing and improving existing speech-to-text algorithms. The aim of this paper is to improve user interaction with the ReaderBench platform, by developing and integrating a speech recognition module designed so that young pupils can dictate their self-explanations to a given text. Afterwards, the ReaderBench framework is used to automatically evaluate the employed reading strategies based on the resulted speech transcriptions. A dataset containing 160 self-explanations from students ranging from 9 to 11 years old was analysed using both original transcripts, and the ones automatically generated by our custom speech recognition system. Multiple methods designed to perform speech recognition are also compared, while a new dedicated model was trained in order to improve the quality of the existing French model for speech recognition from CMUSphinx speech recognition system. Our revised model includes a pronunciation dictionary obtained after training a Long Short-Term Memory (LSTM) Grapheme-to-Phoneme neural network. The accuracy of our system is benchmarked in relation to the automated process of identifying reading strategies implemented in our ReaderBench framework, which is applied on both manual transcriptions and automated speech-to-text inputs. The obtained results argue for the adequacy of our method as the slight decrease in terms of identification accuracy is justifiable in contrast to the effort of manually transcribing each self-explanation.