Characterizing Deletion Transformations Across Dialects Using a Sophisticated Tying Mechanism

We propose a sophisticated tying mechanism for modeling deletion transfonnations between dialects. We empirically show that the proposed tying mechanism reduces deletion errors by 33% when compared to a baseline system using a standard tying mechanism. Statistical tests show that the proposed and baseline models make statistically diOcrcnt errors, thus suggesting that they are complementary systems in dialect recognition tasks. Pronunciation rules learned by our proposed system quantify the occurrence frequency of known rules, and suggest mle candidates for further linguistic studies.