The estimation of the SPP for each individual time-frequency slot is a important part of many speech processing systems. Thus, the widespread speech enhancement approaches based on estimation of the short-time spectral amplitude of the clean speech signal crucially depend on an SPP estimator. However, a reliable SPP estimator is difficult to obtain in a noisy scenario. It is well known that speech signals have characteristic temporal and spectral correlations in the time-frequency domain. Usually, this fact is exploited by smoothing the estimated characteristics, such as the SPP estimations themselves, the a priori SNR, or even the gain factor of individual time-frequency slot across time, frequency, or both. However rather than smoothing the estimates with heuristically chosen filter parameters in a postprocessing step, the correlations can be directly employed in the estimation of the SPP by applying a statistical inference on the a posteriori SNR estimates averaged over a certain adjacent time-frequency slots. Developing of SPP estimators, which are able to take into account spectral correlations in the neighbouring time-frequency slots, has a high priority in our group.