This paper shows improvements in robustness of a speech feature extraction method using Sub-band based Periodicity and Aperiodicity DEcomposition or SPADE. With SPADE, the speech signal is divided into sub-band signals through bandpass filter banks, after which the sub-band signal is decomposed into its periodic and aperiodic features by the comb filter. The evaluation experiment conducted with AURORA-2J (Japanese AURORA-2) shows that SPADE degrades the performance under open-channel condition. To cope with this problem, in this paper we apply the cepstral mean normalization (CMN) to SPADE. The result shows that CMN greatly improves the performance not only for test data under the open-channel condition but also for data under the closed-channel condition. SPADE with CMN achieves an averaged word accuracy of 89.96 %, and an averaged WER reduction of 28.61 %. This word accuracy is better than that achieved by using MFCC with CMN.