Analyzing phonetic variation in the traditional English dialects: Simultaneously clustering dialects and phonetic features

This study explores the linguistic application of bipartite spectral graph partitioning, a graphtheoretic technique that simultaneously identifies clusters of similar localities as well as clusters of features characteristic of those localities. We compare the results using this approach to previously published results on the same dataset using cluster and principal component analysis (Shackleton, 2007). While the results of the spectral partitioning method and Shackleton’s approach overlap to a broad extent, the analyses offer complementary insights into the data. The traditional cluster analysis detects some clusters which are not identified by the spectral partitioning analysis, while the reverse also occurs. Similarly, the principal component analysis and the spectral partitioning analysis detect many overlapping, but also some different linguistic variants. The main benefit of the bipartite spectral graph partitioning method over the alternative approaches remains its ability to simultaneously identify sensible geographical clusters of localities with their corresponding linguistic features.