Application of Cortical Processing Theory to Acoustical Analysis

We developed a computational model of diphone perception based on salient properties of peripheral and central auditory processing. The model comprises an efferent-inspired closed-loop model of the auditory periphery (PAM) connected to a template-matching circuit (TMC). Robustness against background noise is provided principally by the signal processing performed by the PAM, while insensitivity to time-scale variations is provided by properties of the TMC. The PAM parameters were determined in isolation from the TMC. This was achieved by analyzing confusion patterns generated in a paradigm with a minimal cognitive load (the binary Diagnostic Rhyme Test [DRT], with synthetic speech stimuli to restrict phonemic variation). Originally, we intended to test the model by quantifying its ability to predict human performance in perceiving naturally spoken speech in the presence of noise, in two separate tasks: (1) diphone discrimination of minimal word-pairs (Voiers' DRT), and (2) phone identification of schwa-CVC tokens. Eventually, the model was evaluated using synthetic speech material.