A model of dynamic auditory perception and its application to robust speech recognition
Brian P. Strope, Asraa Sadoon Alwan · 2002
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.