A linguistically-informative approach to dialect recognition using dialect-discriminating context-dependent phonetic models

Abstract

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.