In this paper, we propose a speech enhancement technique which compensates for the real and imaginary acoustic spectrograms separately. This technique leverages principal component analysis (PCA) to highlight the clean speech components of the modulation spectra for noise-corrupted acoustic spectrograms. By doing so, we can enhance not only the magnitude but also the phase portions of the complex-valued acoustic spectrogram, thereby creating noise-robust speech features. More particularly, the proposed technique possesses two explicit merits. First, via the operation on modulation domain, the long-term cross-time correlation among the acoustic spectrogram can be captured and subsequently employed to compensate for the spectral distortion caused by noise. Next, due to the individual processing of real and imaginary acoustic spectrograms, the proposed method will not encounter a knotty problem of speech-noise cross-term that usually exists in the conventional acoustic spectral enhancement methods especially when the noise reduction process is inevitable. All of the evaluation experiments are conducted on the Aurora-2 and Aurora-4 databases and tasks. The corresponding results demonstrate that under the clean-condition training setting, our proposed method can achieve performance competitive to or better than many widely used noise robustness methods, including the well-known advanced front-end (AFE), in speech recognition.