1. A method of obtaining normalized voice activity detection features from an audio signal comprising the steps of: at a computing system including a voice activity detector, dividing an audio signal into a sequence of time frames; computing one or more voice activity detection feature of the audio signal for each of the time frames; computing running estimates of minimum and maximum values of the one or more voice activity detection feature of the audio signal for each of the time frames, wherein computing running estimates of minimum and maximum values of the one or more voice activity detection feature comprises applying asymmetrical exponential averaging to the one or more voice activity detection feature; computing input ranges of the one or more voice activity detection feature by comparing the running estimates of the minimum and maximum values of the one or more voice activity detection feature of the audio signal for each of the time frames; mapping the one or more voice activity detection feature of the audio signal for each of the time frames from the input ranges to one or more desired target range to obtain one or more normalized voice activity detection feature; setting smoothing coefficients to correspond to time constants selected to produce one of a gradual change and a rapid change in one of smoothed minimum value estimates and smoothed maximum value estimates; wherein the smoothing coefficients are selected such that at least one of: continuous updating of a maximum value estimate responds rapidly to higher voice activity detection feature values and decays more slowly in response to lower voice activity detection feature values; and continuous updating of a minimum value estimate responds rapidly to lower voice activity detection feature values and increases slowly in response to higher voice activity detection feature values; and wherein the smoothing coefficients are used by the voice activity detector to detect voice activity within the audio signal.