Event detection of speech signals based on auditory processing with a dynamic compressive gammachirp filterbank

To simulate the perceptual extraction of temporal structures of speech, the authors have been proposing an event-plausibilty model that detects the occurrence of subevents in continuous speech signals based on a auditory processing. One of its core components is the filterbank module that simulates the mechanical frequency analysis of the basilar membrane in the cochlea. In this paper, output by the new model using a dynamic compressive gammachirp (dcGC) auditory filterbank was compared with the previous model using a gammatone auditory filterbank. The most important difference between these filters was the nonlinear dynamic level-dependence of the new filter; the previous filterbank was linear. Simulation results revealed that no significant advantage for the new filter (dcGC) was observed for event detection by the event-plausiblity model, which suggests that the algorithm for the event-plausibility model has robustness against differences in peripheral auditory processing.