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Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep autoencoders. To the best of our knowledge, this is the first work that extracts DBNFs for visual speech recognition directly from pixels. We first train a deep autoencoder with a bottleneck layer indoi:10.1109/icassp.2016.7472088 dblp:conf/icassp/PetridisP16 fatcat:ut5tzskabrflzk6mg7t2q6dqpe