Computational Comparison and Classiication of Dialects

In this paper a range of methods for measuring the phonetic distance between dialectal variants are described. It concerns variants of the frequency method, the frequency per word method and Levensh-tein distance, both simple (based on atomic characters) and complex (based on feature bundles). The measurements between feature bundles used Manhattan distance, Euclidean distance or (a measure using) Pearson's correlation coeecient. Variants of these using feature weighting by entropy reduction were systematically compared, as was the representation of diphthongs (as one symbol or two). The dialects were compared with each other directly and indirectly via a standard dialect. The results of comparison were classiied by clustering and by training of a Kohonen map. The results were compared to well-established scholarship in dialectology, yielding a calibration of the methods. These results indicate that the frequency per word method and the Levenshtein distance outperform the frequency method, that feature representations are more sensitive, that Manhattan distance and Euclidean distance are good measures of phonetic overlap of feature bundles, that weighting is not useful, that two-phone representations of diphthongs mostly outperform one-phone representations, and that dialects should be directly compared to each other. The results of clustering give the sharper classiication, but the Kohonen map is a nice supplement.