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Post-filtering methods are used in mobile communications to improve the quality and intelligibility of speech. This paper introduces a noiseadaptive post-filtering algorithm that models the spectral effects observed in natural Lombard speech. The proposed method and another postfiltering technique were compared to unprocessed speech and natural Lombard speech in subjective listening tests in terms of intelligibility and quality. The results indicate that the proposed method outperforms the reference method in difficult noise conditions.