This article presents the results of an analysis of a written corpus with the view of automatically generating it in French Cued Speech (CS). CS is a communication system developed for people with hearing impairment to complement speech reading at the phonetic level using hands. This visual communication mode uses handshapes in different positions near the face in combination with the mouthshape (called ’cues’ or ’keys’) to make the phonemes of spoken language look different from each other. Despite many studies demonstrating its benefits, there are few resources available for learning and practicing it, especially in French. As part of a wider project aimed at creating an online learning platform with automatically generated videos using an augmented reality system displaying a virtual coding, we propose to identify, extract, and analyze 41 textual and phonemic features that might be more complex to (de)code in French CS. For the automatic extraction of complexity, several tools are used: FABRA for readability, SPPAS for phonetization and CS key generation. The results show some strong correlations between readability features, few between phonemic variables, and few between the two types. An initial model is proposed for selecting texts to be recorded for learning French CS.

In this paper, we present HIBOU, an eBook application initially developed for iOs, displaying adapted texts (i.e. simplified), and proposing text comprehension activities. The application has been used in six elementary schools in France to evaluate and train reading fluency and comprehension skills on beginning readers of French. HIBOU displays two versions of French literary and documentary texts from the ALECTOR corpus, the ‘original’, and a simplified version. Text simplifications have been manually performed at the lexical, syntactic, and discursive levels. The child can read in autonomy and has access to different games on word identification. HIBOU is at present being developed to be online in a platform that will be available at elementary schools in France.

In this paper, we present a new parallel corpus addressed to researchers, teachers, and speech therapists interested in text simplification as a means of alleviating difficulties in children learning to read. The corpus is composed of excerpts drawn from 79 authentic literary (tales, stories) and scientific (documentary) texts commonly used in French schools for children aged between 7 to 9 years old. The excerpts were manually simplified at the lexical, morpho-syntactic, and discourse levels in order to propose a parallel corpus for reading tests and for the development of automatic text simplification tools. A sample of 21 poor-reading and dyslexic children with an average reading delay of 2.5 years read a portion of the corpus. The transcripts of readings errors were integrated into the corpus with the goal of identifying lexical difficulty in the target population. By means of statistical testing, we provide evidence that the manual simplifications significantly reduced reading errors, highlighting that the words targeted for simplification were not only well-chosen but also substituted with substantially easier alternatives. The entire corpus is available for consultation through a web interface and available on demand for research purposes.