We propose supervised and unsupervised learning algorithms to extract dialect discriminating phonetic rules and use these rules to adapt biphones to identify dialects. Despite many challenges (e.g., sub-dialect issues and no word transcriptions), we discovered dialect discriminating biphones compatible with the linguistic literature, while outperforming a baseline monophone system by 7.5% (relative). Our proposed dialect discriminating biphone system achieves similar performance to a baseline all-biphone system despite using 25% fewer biphone models. In addition, our system complements PRLM (Phone Recognition followed by Language Modeling), verified by obtaining relative gains of 15-29% when fused with PRLM. Our work is an encouraging first step towards a linguistically-informative dialect recognition system, with potential applications in forensic phonetics, accent training, and language learning.