Are Sounds Sound for Phylogenetic Reconstruction? (2024)

Abstract: In traditional studies on language evolution, scholars often emphasize the importance of sound laws and sound correspondences for phylogenetic inference of language family trees. However, to date, computational approaches have typically not taken this potential into account. Most computational studies still rely on lexical cognates as major data source for phylogenetic reconstruction in linguistics, although there do exist a few studies in which authors praise the benefits of comparing words at the level of sound sequences. Building on (a) ten diverse datasets from different language families, and (b) state-of-the-art methods for automated cognate and sound correspondence detection, we test, for the first time, the performance of sound-based versus cognate-based approaches to phylogenetic reconstruction. Our results show that phylogenies reconstructed from lexical cognates are topologically closer, by approximately one third with respect to the generalized quartet distance on average, to the gold standard phylogenies than phylogenies reconstructed from sound correspondences.

## introduction although controversially discussed in the beginning @xcite , quantitative approaches to phylogenetic reconstruction based on bayesian phylogenetic inference frameworks have now become broadly accepted and used in the field of comparative linguistics. this is reflected by the increasing number of computer-based phylogenies that have been proposed for the world's largest language families -dravidian @xcite , sino-tibetan @xcite , and indo-european @xcite -and even fully automated workflows, in which even cognate words are identified automatically, have shown to be comparatively robust @xcite . while rarely practiced in the pre-computational past of historical linguis-tics, computing detailed, fully resolved phylogenies with branch lengths and at times even estimated divergence times, has now become a routine tasks in contemporary language evolution studies. although traditional scholars have started to accept computational language phylogenies as a new tool deserving its place in the large tool chain of comparative linguistics, scholars still express substantial skepticism against most language phylogenies that have been inferred so far. one of the major arguments typically mentioned in this context is that phylogenetic approaches are usually based on cognate sets (sets of historically related words) that are identified in semantically aligned word lists. since these cognate sets reflect lexical data only, many scholars mistrust them, given that lexical data are assumed to be substantially less stable over time than other aspects of languages @xcite . yet, for being able to infer stable phylogenetic trees a mix of conserved characters and more variable characters might be more beneficial. in classical historical linguistics, the data used for subgrouping are traditionally composed of small collections of so-called shared innovations @xcite . what counts as a shared innovation has itself never been clearly defined in the literature, but the largest amount of data used by scholars is traditionally taken from sound correspondences or supposed sound change processes (compare, for example the data in @xcite . although it is controversially debated in the field @xcite , many classical linguists still emphasize that sound correspondences are largely superior to lexical data to determine subgrouping. there have only been few attempts to assess how well quantitative approaches to phylogenetic reconstruction perform when using sound correspondences instead of lexical cognates @xcite . the main reason is that encoding data to compute phylogenies from sound change patterns is tedious and labour-intensive even for a dataset comprising only 20 languages. therefore, there have been but a few attempts to assess how well quantitative approaches to phylogenetic reconstruction perform when using sound correspondences instead of lexical cognates. here we build on state-of-the-art methods for automatic cognate detection and phonetic alignment in historical linguistics @xcite and combine them with novel approaches for inferring sound correspondence patterns in multilingual datasets @xcite . using this machinery we have devised a new workflow for phylogenetic reconstruction based on sound correspondence patterns. with a new collection of ten gold standard datasets, we test our workflow and compare it with alternative workflows that are exclusively based on lexical data. our results indicate that sound correspondence patterns are substantially less suitable for the purpose of computer-based phylogenetic reconstruction than postulated. ## background the majority of previous work on phylogenetic reconstruction using bayesian phylogenetic infer-ence @xcite @xcite is based on cognate sets that are encoded as binary vectors. the presence or absence of a language in a cognate set is thus encoded as 1 or 0, respectively. subsequently, phylogenetic trees are inferred by assuming that cognate sets evolve along a phylogenetic tree via a gain and loss processes (see figure 1 ). the binary-state encoding is the most frequently used encoding technique; we deploy it in this study as well. once such a dataset has been assembled, binary state data evolution can be modeled via a time-reversible binary state continuous time markov chain model (binary-ctmc, @xcite , which allows for gain and loss events to occur for an arbitrary number of times. branch lengths on these trees reflect the mean number of expected substitutions (gain/loss events) per binary character site. the major contributions of this study are: (1) we provide an automated workflow that allows to infer cognates and correspondence patterns and analyze them with the help of bayesian phylogenetic inference methods, (2) we cross-validate the bayesian inference results via maximum likelihood (ml) tree reconstructions and thereby discover that default bayesian priors that typically work well on molecular data can induce a prior bias when analyzing language datasets, (3) we show how the quality of phylogenetic reconstruction approaches based on sound correspondences can be compared to phylogenetic reconstruction based on lexical data, and in this way, and (4) we put the debate about the usefulness of sound-based as opposed to cognatebased phylogenies to the test. as an early example for sound-based approaches to phylogenetic reconstruction, hruschka et al. (2015) apply a ctmc model that allows for transitions between a fixed number of sounds for detecting the important sound changes in a dataset comprising etymologies across turkic languages. hruschka et al. do not infer phylogenies from their data. instead, they use an established phylogeny (such established phylogenies are not readily available for many language families of the world) to infer branch lengths and transition probabilities between sounds in their data in order to detect sound changes at different time points in a time-calibrated family tree of turkic. wheeler and whiteley (2015) start from typical word lists (that would otherwise be used in phylogenetic reconstruction based on lexical data) and apply a parsimony-based algorithm that aligns words regardless if they are cognate or not, reconstructs a hypothetical ancestral word from the alignment, and seeks to infer the phylogeny that explains the observed sequences via the minimum amount of changes/mutations @xcite . in a later study, @xcite apply the same approach to a dataset of bantu languages. the method by @xcite is linguistically debatable, since words are not assigned to cognate sets prior to aligning them. it is well known that there is a strict difference between regular sound change processes and processes resulting from lexical replacement @xcite and that even words that are cognate are not necessarily fully alignable @xcite . @xcite start from manually extracted sound correspondence patterns for consonants in a dataset of 21 tukanoan languages, to which proto-forms had also been manually added. based on these sound correspondence patterns, they apply-in analogy to @xcite -an algorithm that searches for the tree that provides the most parsimonious explanation for sound evolution. in contrast to @xcite , however, they added specific constraints for the transitions from one sound to another sound, which were based on expert judgments for the tukanoan language family. the approach by chacon and @xcite , finally, requires an enormous amount of preprocessing that entails the risk of inducing circular results, since proto-forms and major directions of sound change processes are required to be known in advance. while all approaches exhibit individual shortcomings, one of the largest shortcomings lies in the fact that it is very difficult to apply them systematically. this is also supported by the observation that no additional analogous studies have been conducted by other teams, despite the fact that all of the above methods have been proposed years ago. ## discussion and conclusion while our results are less conclusive than one might expect, we think that they show clearly enough that sound-correspondence-based phylogenies should be taken with care. as we show, sound-correspondence-based phylogenies do rarely substantially outperform cognate-based phylogenies. instead, we observe that cognate-based phylogenies are topologically much closer to the gold standard on average. at this point, we cannot say, whether combined approaches significantly outperform phylogenies purely inferred from cognate sets. future studies that expand the data we used in this study are needed to clarify this question. given the prior bias we observed for default parameter priors that work well for bayesian inference on molecular data, we advocate for a critical re-assessment of all priors that are being routinely used in bayesian analyses of language data. this reassessment can be conducted by routinely executing analogous ml analyses and carefully inspecting all ml parameter estimates (branch lengths, tree length, base frequencies, α shape parameter) and not only focusing on the resulting tree topology. we also cross-checked the estimates for the base frequencies, but did not observe any discrepancies between ml and bayesian inference as a flat default (β(1, 1)) prior was used. the reasons for the extreme bi-modal distribution of α values we observed remain unclear, despite the fact that we have assessed 30 different dataset characteristics and summary statistics that are, however, all uncorrelated with the α estimate. using machine learning techniques to predict α values for datasets and thereby potentially understand the dataset properties responsible for this bi-modal distribution is not feasible due to an insufficient amount of available data. investigating this issue hence remains subject of future work.