Motivated by the human auditory system, a feature extraction method for automatic speech recognition (ASR) based on the differential processing strategy of the AVCN, PVCN and the DCN of the cochlear nucleus is proposed. The method utilizes a zero-crossing with peak amplitudes (ZCPA) auditory model as synchrony detector to discriminate the low frequency formants. It utilizes the mean rate information in the synapse processing to capture the very rapidly changing dynamic nature of speech. Additionally, a temporal companding method is utilized for spectral enhancement through two-tone suppression. We propose to separate synchrony detection from synaptic processing as observed in the parallel processing methodology in the cochlear nucleus. HMM recognition using isolated digits showed improved recognition rates in clean and in non-stationary noise conditions than the existing auditory model.
Bibliographic reference. Haque, Serajul / Togneri, Roberto (2010): "A feature extraction method for automatic speech recognition based on the cochlear nucleus", In INTERSPEECH-2010, 2454-2457.