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Cepstral mean subtraction (CMS) is a well-known feature domain channel compensation technique employed to eliminate the effects of convolutive channel distortion. However, as the authors use in log-spectral mean subtraction (LSMS), the compensation might be applied in spectral domain before the filter-bank analysis with a higher-frequency resolution. LSMS can also be combined with CMS to further improve the recognition performance. In this study, the authors compare the performances of LSMS and CMS methods using a multi-channel, text-dependent single utterance speaker recognition database. In the experiments, the authors observe that LSMS outperforms CMS especially in the high false acceptance region. Moreover, the accuracy is further improved when the methods are combined together. With the combination, the authors achieve 15.5% relative reduction in equal error rate for no score normalisation and 9.4% for test normalisation cases when compared with the baseline CMS experiment.