Supervised and unsupervised feature extraction from a cochlear model for speech recognition

The authors explore the application of a novel classification method that combines supervised and unsupervised training, and compare its performance to various more classical methods. The authors first construct a detailed high dimensional representation of the speech signal using Lyon´s cochlear model and then optimally reduce its dimensionality. The resulting low dimensional projection retains the information needed for robust speech recognition