Most of the features of Cover Song Identification (CSI), for example, Pitch Class Profile (PCP) related features, are based on the musical facets shared among cover versions: melody evolution and harmonic progression. In this work, the perceptual feature was studied for CSI. Our idea was to modify the Perceptual Linear Prediction (PLP) model in the field of Automatic Speech Recognition (ASR) by (a) introducing new research achievements in psychophysics, and (b) considering the difference between speech and music signals to make it consistent with human hearing and more suitable for music signal analysis. Furthermore, the obtained Linear Prediction Coefficients (LPCs) were mapped to LPC cepstrum coefficients, on which liftering was applied, to boost the timbre invariance of the resultant feature: Modified Perceptual Linear Prediction Liftered Cepstrum (MPLPLC). Experimental results showed that both LPC cepstrum coefficients mapping and cepstrum liftering were crucial in ensuring the identification power of the MPLPLC feature. The MPLPLC feature outperformed state-of-the-art features in the context of CSI and in resisting instrumental accompaniment variation. This study verifies that the mature techniques in the ASR or Computational Auditory Scene Analysis (CASA) fields may be modified and included to enhance the performance of the Music Information Retrieval (MIR) scheme.