Power function-based power distribution normalization algorithm for robust speech recognition

Abstract

A novel algorithm that normalizes the distribution of spectral power coefficients is described in this paper. The algorithm, called power-function-based power distribution (PPDN) is based on the observation that the ratio of arithmetic mean to geometric mean changes as speech is corrupted by noise, and a parametric power function is used to equalize this ratio. We also observe that a longer “medium-duration” observation window (of approximately 100 ms) is better suited for parameter estimation for noise compensation than the briefer window typically used for automatic speech recognition. We also describe the implementation of an online version of PPDN based on exponentially weighted temporal averaging. Experimental results shows that PPDN provides comparable or slightly better results than state of- the-art algorithms such as vector Taylor series for speech recognition while requiring much less computation. Hence, the algorithm is suitable for both real-time speech communication or as a real-time preprocessing stage for speech recognition systems.