Analysis of Dialectal Influence in Pan-Arabic ASR

In this paper, we analyze the impact of five Arabic dialects on the front-end and pronunciation dictionary components of an Automatic Speech Recognition (ASR) system. We use ASR's phonetic decision tree as a diagnostic tool to compare the robustness of MFCC and MLP front-ends to dialectal variations in the speech data and found that MLP Bottle-Neck features are less robust to such variations. We also perform a rule-based analysis of the pronunciation dictionary, which enables us to identify dialectal words in the vocabulary and automatically generate pronunciations for unseen words. We show that our technique produces pronunciations with an average phone error rate 9.2%.