A tentative model for dimensionless phoneme distance from binary distinctive features
This work addresses a methodological gap in computational linguistics by providing a model for phoneme distance calculation, which is incremental as it builds on existing feature-based approaches.
The paper tackles the problem of quantifying distances between phonemes using binary distinctive features, proposing a model that yields linearly consistent distances for use in phoneme sequence alignment and as prior probabilities in Bayesian phylogenetic analyses for language relationships.
This work proposes a tentative model for the calculation of dimensionless distances between phonemes; sounds are described with binary distinctive features and distances show linear consistency in terms of such features. The model can be used as a scoring function for local and global pairwise alignment of phoneme sequences, and the distances can be used as prior probabilities for Bayesian analyses on the phylogenetic relationship between languages, particularly for cognate identification in cases where no empirical prior probability is available.