Speech kurtosis estimation from observed noisy signal based on generalized Gaussian distribution prior and additivity of cumulants

In this paper, we propose a new method for stable estimation of the kurtosis of a speech power spectrum. Speech kurtosis can be used for the prediction of speech recognition accuracy as reported in recent studies. However, the conventional estimation method is very unstable owing to the high sensitivity of higher-order statistics. To overcome this problem, we introduce the generalized Gaussian distribution prior in order to avoid the calculation of higher-order statistics, and construct a kurtosis table that directly represents the relationship among the kurtosis of speech, noise, and their mixture in the power spectrum domain. Speech kurtosis can be estimated stably from observable data by looking up values in the table. An experimental evaluation confirms the efficacy of the proposed method.