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This paper presents a novel feature extraction scheme taking advantage of both the nonlinear modulation speech model and the spatial diversity of speech and noise signals in a multisensor environment. Herein, we propose applying robust features to speech signals captured by a multisensor array minimizing a noise energy criterion over multiple frequency bands. We show that we can achieve improved recognition performance by minimizing the Teager-Kaiser energy of the noise-corrupted signals in different frequency bands. These Multiband, Multisensor Cepstral (MBSC) features are inspired by similar ones already been applied to single-microphone noisy Speech Recognition tasks with significantly improved results. The recognition results show that the proposed features can perform better than the widely-used MFCC features.