Short-time Gaussianization for robust speaker verification

In this paper, a novel approach for robust speaker verification, namely short-time Gaussianization, is proposed. Short-time Gaussianization is initiated by a global linear transformation of the features, followed by a short-time windowed cumulative distribution function (CDF) matching. First, the linear transformation in the feature space leads to local independence or decorrelation. Then the CDF matching is applied to segments of speech localized in time and tries to warp a given feature so that its CDF matches normal distribution. It is shown that one of the recent techniques used for speaker recognition, feature warping [1] can be formulated within the framework of Gaussianization. Compared to the baseline system with cepstral mean subtraction (CMS), around 20% relative improvement in both equal error rate (EER) and minimum detection cost function (DCF) is obtained on NIST 2001 cellular phone data evaluation.