  Hard of hearing or profoundly deaf people make use of cued speech (CS) as a
communication tool to understand spoken language. By delivering cues that are
relevant to the phonetic information, CS offers a way to enhance lipreading. In
literature, there have been several studies on the dynamics between the hand
and the lips in the context of human production. This article proposes a way to
investigate how a neural network learns this relation for a single speaker
while performing a recognition task using attention mechanisms. Further, an
analysis of the learnt dynamics is utilized to establish the relationship
between the two modalities and extract automatic segments. For the purpose of
this study, a new dataset has been recorded for French CS. Along with the
release of this dataset, a benchmark will be reported for word-level
recognition, a novelty in the automatic recognition of French CS.
