Point process models of spectro-temporal modulation events for speech recognition

Neurobiological research has uncovered the existence of cortical neurons in various animal species tuned to particular spectro-temporal modulations (STM) in the auditory stimulus. Other findings indicate that temporal statistics of the resulting neural spike trains may encode the underlying content of species-specific communication calls. With this motivation, we present an alternative approach to speech recognition based on point process statistical models of the local maxima events produced by a cortically-inspired spectro-temporal filter bank. We demonstrate the computational adequacy of this approach on the practical task of keyword spotting.