A model of dynamic auditory perception and its application to robust speech recognition

This paper derives a non-linear model of dynamic auditory perception. The model consists of a linear filter bank with carefully-parameterized logarithmic additive adaptation after each filter output. An extensive series of perceptual forward masking experiments, together with previously reported forward masking data, determine the model´s dynamic parameters. The model´s prediction error of forward masking data has a standard deviation of less than 3.3 dB across wide ranging frequencies, input levels, and probe delay times. We present an initial evaluation of the dynamic model as a front end for an isolated word recognition system, and show an improvement in the robustness to background noise when compared to MFCC and LPCC front ends