Cross-lingual acoustic modeling for dialectal Arabic speech recognition

Abstract

Amajor 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 graphemebased acoustic modeling were investigated. To make phonemebased 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.