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Hard of hearing or profoundly deaf people make use of cued speech (CS) as acommunication tool to understand spoken language. By delivering cues that arerelevant to the phonetic information, CS offers a way to enhance lipreading. Inliterature, there have been several studies on the dynamics between the handand the lips in the context of human production. This article proposes a way toinvestigate how a neural network learns this relation for a single speakerwhile performing a recognition task using attention mechanisms. Further, ananalysis of the learnt dynamics is utilized to establish the relationshipbetween the two modalities and extract automatic segments. For the purpose ofthis study, a new dataset has been recorded for French CS. Along with therelease of this dataset, a benchmark will be reported for word-levelrecognition, a novelty in the automatic recognition of French CS.

Investigating the dynamics of hand and lips in French Cued Speech using attention mechanisms and CTC-based decoding