The Minimal-Pair ABX (MP-ABX) paradigm has been proposed as a method for evaluating speech features for zero-resource/unsupervised speech technologies. We apply it in a phoneme discrimination task on the Articulation Index corpus to evaluate the resistance to noise of various speech features. In Experiment 1, we evaluate the robustness to additive noise at different signal-to-noise ratios, using car and babble noise from the Aurora-4 database and white noise. In Experiment 2, we examine the robustness to different kinds of convolutional noise. In both experiments we consider two classes of techniques to induce noise resistance: smoothing of the time-frequency representation and short-term adaptation in the time-domain. We consider smoothing along the spectral axis (as in PLP) and along the time axis (as in FDLP). For short-term adaptation in the time-domain, we compare the use of a static compressive non-linearity followed by RASTA filtering to an adaptive compression scheme.