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Cued speech is a visual mode of communication that uses handshapes and placements in combination with the mouth movements of speech to make the phonemes of a spoken language look different from each other and clearly understandable to deaf and hearing-impaired people. The aim of cued speech is to overcome the problems of lip reading and thus enable deaf children and adults to wholly understand spoken language. Cued speech recognition requires hand gesture recognition and lip shape recognition, and also integration of the two components. This article presents hidden Markov model (HMM)-based vowel recognition as used in Cued Speech for French. Based on concatenative feature fusion and multistream HMM decision fusion, lip shape and hand position components were integrated into a single component, and automatic vowel recognition was realized. In the case of multistream HMM decision fusion, the obtained vowel classification accuracy using lip shape and hand position information was 87.6%, showing absolute improvement of 19.6% in comparison with a use restricted only to lip parameters.