This paper presents new results about the robustness of invariantintegration features (IIF) in noisy conditions. Furthermore, it is shown that a feature-enhancement method known as "power-bias subtraction" for noisy conditions can be combined with the IIF approach to improve its performance in noisy environments while keeping the robustness of the IIFs to mismatching vocal-tract length training-testing conditions. Results of experiments with training on clean speech only as well as experiments with matchedcondition training are presented.