This paper compares several particle filtering variants for speech feature enhancement in non-stationary noise environments. By analyzing the random processes of clean speech, noise and noisy speech, appropriate proposal densities are derived. The performances of the resulting particle filters, i.e. modified Sampling-Importance-Resampling (mod-SIR), auxiliary SIR and likelihood particle filter, are compared in terms of word accuracy achieved by the subsequent speech recognizer on the AURORA 2 database. It turns out that for the noises found in this database, noise compensation techniques that assume stationary noise work equally well.