A physiologically motivated front-end for speech recognition

A physiological front-end preprocessor for speech recognition was evaluated using a large isolated-word database in quiet and noise. The front-end was based on the ensemble interval histogram (EIH) model developed by O. Ghitza. This model provides phase or synchrony information similar to that available on the auditory nerve. A modified EIH front-end was implemented and was tested using the Lincoln robust hidden Markov model isolated-word recognizer with a multistyle database at various signal-to-noise ratios (SNRs). The modified EIH front-end performed as well as a conventional mel-filter-bank front-end for normal speech. It provided a slight improvement in error rate at very low SNRs but required substantially more computation than the mel-filter-bank front-end