Evaluating the pairwise string alignment of pronunciations

Abstract

Pairwise string alignment (PSA) is an important general technique for obtaining a measure of similarity between two strings, used e.g., in dialectology, historical linguistics, transliteration, and in evaluating name distinctiveness. The current study focuses on evaluating different PSA methods at the alignment level instead of via the distances it induces. About 3.5 million pairwise alignments of Bulgarian phonetic dialect data are used to compare four algorithms with a manually corrected gold standard. The algorithms evaluated include three variants of the Levenshtein algorithm as well as the Pair Hidden Markov Model. Our results show that while all algorithms perform very well and align around 95% of all alignments correctly, there are specific qualitative differences in the (mis)alignments of the different algorithms.

Computational analysis of Gondi dialects

“…In dialectometry (Wieling et al, 2009), the segment-segment similarity matrix is estimated using pointwise mutual information (PMI). The PMI score for two sounds x and y is defined as followed:…”

Synchronic patterns of Tuscan phonetic variation and diachronic change: Evidence from a dialectometric study

“…Hanks, 1990). This method was introduced by Wieling et al (2009) and found to yield superior alignments as well as acoustically sensible sound correspondences (Wieling et al, to appear). 5 As multiple speakers were interviewed in every location, we used the most frequent phonetic variant as representative of all attested PVs for every normalized form.…”

Measuring Language Closeness by Modeling Regularity

“…While the original Levenshtein edit distance is based on these three operations without any restrictions, later algorithms adapt this method by additional edit operations or restrictions. Wieling et al (2009) compare several alignment algorithms applied to dialect pronunciation data. These algorithms include several adaptations of the Levenshtein algorithm and the Pair Hidden Markov Model.…”

“…Levenshtein algorithm with distances based on PMI: Wieling et al (2009) use Point-wise Mutual Information (PMI) as the basis for segment distances. They assign different costs to segments, and use the entire dataset for each alignment.…”

“…All alignment algorithms based on Levenshtein distance evaluated by Wieling et al (2009) restrict aligning vowels with consonants.…”