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Speech enhancement schemes rely generally on the knowledge of the noise power spectral density. The estimation of these statistics is particularly a critical issue and a challenging problem under non-stationary noise conditions. With this respect, subspace based approaches have shown to allow for reduced estimation delay and perform a good tracking vs. final misadjustment tradeoff. One key attribute for noise floor tracking is the estimation bias: an overestimation leads to over-suppression and to more speech distortion; while an underestimation leads to a high level of residual noise. The present paper investigates the bias of the subspace-based scheme, and particularly the robustness of the bias compensation factor to the desired speaker characteristics and the input SNR.