In this study, we perform a theoretical analysis of the amount of musical noise generated in Bayesian minimum mean-square error speech amplitude estimators. In our previous study, a musical noise assessment based on kurtosis has been successfully applied to spectral subtraction. However, it is difficult to apply this approach to the methods with a decision-directed a priori SNR estimator because it corresponds to a nonlinear recursive process for noise power spectral sequences. Therefore, in this paper, we analyze musical noise generation by combining Breithaupt-Martinfs approximation and our higher-order-statistics analysis. We also compare the result of theoretical analysis and that of objective experimental evaluation to indicate the validity of the proposed closed-form analysis.