In this paper, hidden Markov models (HMM)-based vowel and consonant automatic recognition in Cued Speech for French are presented. Cued Speech is a visual communication mode which uses handshapes in different positions and in combination with lip-patterns of speech, makes all the sounds of spoken language clearly understandable to deaf and hearing-impaired people. The aim of Cued Speech is to overcome the problems of lipreading and thus enable deaf children and adults to fully understand a spoken language. Previously, the authors have reported experimental results on vowel recognition in Cued Speech for French. This study, further investigates the vowel recognition, and also reports automatic consonant recognition experiments in Cued Speech for French. In addition, isolated word recognition experiments both in normal-hearing and deaf subject are presented, showing a promising word accuracy of 92% on average.
['Panikos Heracleous', 'Noureddine Aboutabit', 'Denis Beautemps']
HMM-based vowel and consonant automatic recognition in Cued Speech for French