In this article we approximate the clean speech spectral magnitude as well as noise spectral magnitude with a mixture of Gaussians pdfs using the Expectation-Maximization algorithm (EM). Subsequently, we apply the Bayesian inference framework to the degraded spectral coefficients and by employing Minimum Mean Square Error Estimation (MMSE), we derive a closed form solution for the spectral magnitude estimation task adapted to the spectral characteristics and noise variance of each band. We evaluate our algorithm using true, coloured, slowly and quickly varying noise types (Factory and aircraft noise) and demonstrate its robustness at very low SNRs.