Novel robust feature extraction based on spectrally masked channel energy ratio (SMaChER) for speech recognition

Comparing speech recognition performance between human beings and computer, the latter´s performance degrades dramatically in a noisy mobile environment. Based on the perceptual study of speech sounds in terms of auditory masking and speech perception study, it has been suggested that the contribution of each auditory channel is rather independent as used in articulation index model. From the point of view of the signal process perspective, we can diminish the noise caused variability by using spectral masking so that the noise signal between spectrum gaps is masked and limit the error propagation between frequency (auditory) channels. Based on this observation, we propose a novel robust feature extraction based spectrally masked channel energy ratio (SMaChER). We show the significant error reduction rate on noisy data has been achieved.