In a previous work, we have successfully integrated the transformation-based signal subspace technique with the generalized singular value decomposition (GSVD) algorithm to develop an improved speech enhancement framework [1]. In this paper, we further incorporate the perceptual masking effect of the psychoacoustics model as extra constraints of the previously proposed GSVD-based algorithm to obtain improved sound feature, and furthermore make sure the undesired residual noise to be nearly unperceivable. Both subjective listening tests and spectrogram-plot comparison showed that the closed-form solution developed here can offer significantly better speech quality than either the conventional spectral subtraction algorithm or the previously proposed GSVD-based technique, regardless of whether the additive noise is white or not.