Speech intelligibility prediction based on a physiological model of the human ear and a hierarchical spiking neural network.

A speech intelligibility (SI) prediction model is proposed that includes an auditory preprocessing component based on the physiological anatomy and activity of the human ear, a hierarchical spiking neural network, and a decision back-end processing based on correlation analysis. The auditory preprocessing component effectively captures advanced physiological details of the auditory system, such as retrograde traveling waves, longitudinal coupling, and cochlear nonlinearity. The ability of the model to predict data from normal-hearing listeners under various additive noise conditions was considered.