A major problem with dialectal Arabic acoustic modeling is due to the very sparse available speech resources. In this paper, we have chosen Egyptian Colloquial Arabic (ECA) as a typical dialect. In order to benefit from existing Modern Standard Arabic (MSA) resources, a cross-lingual acoustic modeling approach is proposed that is based on supervised model adaptation. MSA acoustic models were adapted using MLLR and MAP with an in-house collected ECA corpus. Phoneme-based and grapheme-based acoustic modeling were investigated. To make phoneme-based adaptation feasible, we have normalized the phoneme sets of MSA and ECA. Since dialectal Arabic is mainly spoken, graphemic form usually does not match actual spelling as in MSA, a graphemic MSA acoustic model was used to force align and to choose the correct ECA spelling from a set of automatically generated spelling variants lexicon. Results show that the adapted MSA acoustic models outperformed acoustic models trained with only ECA data.