This post introduces a model of segmental/distinctive features for the symbolic representation of sounds, covering almost 600 segments from CLTS (List et al., 2019) mapped to unique sets of bivalent features. It is being designed as an alternative input to vectors of presence/absence built from BIPA descriptors, analogous to other feature matrices like the one by Phoible (Moran & McCloy, 2019). While still under development, it can already be used both for training models of machine learning and statistics, notably decision trees, and for bootstrapping language- and process-specific models, aided by an “universal” and concise reference. The complete matrix is available on Zenodo. Asupporting Python library, distfeat, is available on PyPI.
Having seen how we can obtain a simple scorer derived from the feature system in CLTS (List et al. 2019) in last month’s post, what is missing now, in order to use the scorer for alignment analyses, is an alignment function which can take the scorer as an argument. If one does not have higher ambitions with respect to the alignment function itself, this step can be achieved in a very straightforward way with help of LingPy’s (List et al. 2018)
sw_align() method. As can be seen from the documentation, this method takes as input two sequences (i.e., lists of sounds), along with a scoring function. Obviously, all we need to do now is to create our specific scorer based on the CLTS features, and then pass this scoring function along with our sequences to the function.
In the past, people have repeatedly asked me how they could use their own scoring functions in combination with LingPy’s alignment algorithms. Their major concern was that the sound-class-based scoring systems we use in LingPy might fail to reflect true phonetic similarity of sounds, specifically also because they are not informed by classical ideas about distinctive features in phonology. As described in detail in List (2014), LingPy converts sounds in phonetic transcription to an internal alphabet of less than 30 letters, to which the alignment algorithms are then applied in a second stage.