Short-time kurtosis of speech signals with application to co-channel speech separation

Recent work into the separation of mixtures of speech signals has shown some success. One particular method is based on the assumption that scalar mixtures of speech signals have a kurtosis less than that for either source. Under this assumption, a simple gradient search algorithm is employed to maximize kurtosis thereby separating the source speech signals from the mixture. While this assumption has been observed to be generally true for long speech segments, it is quite reasonable to expect the assumption not to hold over short segments (windows) of speech. In this case, kurtosis maximization is not the appropriate strategy and the algorithm will fail to separate the signals. We examine the kurtosis of speech signals over short segments of speech, i.e., short-time kurtosis. The analysis indicates in general, how successful a kurtosis maximization strategy can be in separating speech signals from a mixture.