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This paper proposes a simple and effective approach for automatic recognitionof Cued Speech (CS), a visual communication tool that helps people with hearingimpairment to understand spoken language with the help of hand gestures thatcan uniquely identify the uttered phonemes in complement to lipreading. Theproposed approach is based on a pre-trained hand and lips tracker used forvisual feature extraction and a phonetic decoder based on a multistreamrecurrent neural network trained with connectionist temporal classificationloss and combined with a pronunciation lexicon. The proposed system isevaluated on an updated version of the French CS dataset CSF18 for which thephonetic transcription has been manually checked and corrected. With a decodingaccuracy at the phonetic level of 70.88%, the proposed system outperforms ourprevious CNN-HMM decoder and competes with more complex baselines.

Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding