We present a frame-synchronous noise compensation algorithm that uses Stochastic Matching approach to cope with time-varying unknown noise. This method proposes to estimate simple mapping function in parallel with Viterbi alignment. The technique is entirely general since no assumption is made on the nature, level and variation of noise. Our algorithm is evaluated on the VODIS database recorded in a moving car. For various tasks, our technique outperforms significantly classical methods. For instance, using the affine transformation the proposed algorithm gives an error rate improvement of 13.3% compared to Parallel Model Combination (PMC), 15.5% on Spectral Subtraction (SS) and 27.8% on frame-synchronous Mean Cepstre Removal (MCR) for the numbers recognition task in real noise.