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A novel method for speech enhancement based on Convolutive Non-negative Matrix Factorization (CNMF) is presented in this paper. The sparsity of activation matrix for speech components has already been utilized in NMF-based enhancement methods. However such methods do not usually take into account prior knowledge about occurrence relations between different speech components. By introducing the notion of cosparsity, we demonstrate how such relations can be characterized from available speech data and enforced when recovering speech from noisy mixtures. Through objective evaluations we show our proposed regularization improves sparse reconstruction of speech, especially in low SNR conditions.