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The work presented in this paper is motivated by our earlier work exploring sparse representation based approach for automatic bandwidth extension (ABWE) of speech signals. In that work, two dictionaries one for voiced and the other for unvoiced speech frames are created using KSVD algorithm on wideband data. Each of the atoms of these dictionaries is then decimated and interpolated by a factor of 2 to generate narrowband interpolated (NBI) dictionaries whose atoms have one-to-one correspondence with those of the WB dictionaries. The given narrowband speech frames are also interpolated to generated NBI targets and those are sparse coded over the NBI dictionaries. The resulting sparse codes are then applied to the WB dictionaries to estimate the WB target data. In this work, we extend the said approach by making use of an existing semi-coupled dictionary learning (SCDL) algorithm. Unlike the direct dictionary learning, the SCDL algorithm also learns a set of bidirectional transforms coupling the dictionaries more flexibly. The bandwidth enhanced speech obtained employing the SCDL approach and a modified high/low band gain adjustment yields significant improvements in terms of speech quality measures as well as in the context of children’s mismatched speech recognition.