Analyzing and Interpreting Automatically Learned Rules Across Dialects

In this paper, we demonstrate how informative dialect recognition systems such as acoustic pronunciation model (APM) help speech scientists locate and analyze phonetic rules efficiently. In particular, we analyze dialect-specific characteristics automatically learned from APM across two American English dialects. We show that unsupervised rule retrieval performs similarly to supervised retrieval, indicating that APM is useful in practical applications, where word transcripts are often unavailable. We also demonstrate that the top-ranking rules learned from APM generally correspond to the linguistic literature, and can even pinpoint potential research directions to refine existing knowledge. Thus, the APM system can help phoneticians analyze rules efficiently by characterizing large amounts of data to postulate rule candidates, so they can reserve time to conduct more targeted investigations. Potential applications of informative dialect recognition systems include forensic phonetics and diagnosis of spoken language disorders.