incom.py 2.0 - Calculating Linguistic Distances and Asymmetries in Auditory Perception of Closely Related Languages (2021)

Abstract: We present an extended version of a tool developed for calculating linguistic distances and asymmetries in auditory perception of closely related languages. Along with evaluating the metrics available in the initial version of the tool, we introduce word adaptation entropy as an additional metric of linguistic asymmetry. Potential predictors of speech intelligibility are validated with human performance in spoken cognate recognition experiments for Bulgarian and Russian. Special attention is paid to the possibly different contributions of vowels and consonants in oral intercomprehension. Using incom.py 2.0 it is possible to calculate, visualize, and validate three measurement methods of linguistic distances and asymmetries as well as carrying out regression analyses in speech intelligibility between related languages.

## introduction for example, both similarity of phoneme inventories and lexical similarity were identified as factors predicting the perceptual confusability (and, by implication, similarity) of languages in the great language game @xcite . these authors report confusion asymmetries within the set of slavic languages, i.e., cases of speakers of one language (e.g. bulgarian) understanding another language (e.g. russian) better than the other way round. phonetic and lexical distances are also considered as determinants of mutual intelligibility in the work of gooskens and colleagues, who use a game-like interface, micrela foot_1 , to collect data for germanic, romance, and (six) slavic languages @xcite . notions of entropy and surprisal @xcite are employed in the incomslav framework foot_2 to measure information density and gauge how language users master high degrees of surprisal due to partial incomprehensibility of linguistic encodings. the key idea is that understanding an unknown but related lx should be better when the l1 language model adapted for processing the unknown lx exhibits relatively low average surprisal, or information density @xcite @xcite . ## incom.py 2.0 we borrow notation from @xcite and let l denote a language such as russian or bulgarian. each language l has an associated alphabet -a set of characters -a(l) which includes the special symbol ∅. we use @xmath0. similarly, we will use ipa(w) to denote the symbolic phonetic representation of w, with a ipa (l) being the phonetic alphabet of l and s @xmath1. moreover, let v(ipa(w)) denote the set of all vowels in w and c(ipa(w)) the set of all consonants in w, respectively. ld and was following @xcite , incom.py 2.0 supports computing the levenshtein distance (ld) and wordadaptation surprisal (was) as well as their normalized versions nld and nwas between a pair of words w i , w j on the orthographic level. we extend these to the phonetic level by computing (n)ld and (n)was between ipa(w i ) and ipa(w j ), v(ipa(w i )) and v(ipa(w j )) as well as c(ipa(w i )) and c(ipa(w j )). we refer to @xcite for the mathematical definitions an in-depth discussion of (n)ld and (n)was. ## experimental setup via the incomslav platform 5 , we conducted a series of mutual intelligibility tests in the spo-ken modality with isolated cognate (i.e., historically or etymologically related) words. being a challenging task on its own, the context-free recognition of cross-lingual cognates is a precondition of successful oral intercomprehension @xcite . here, we investigate the transparency of bulgarian-russian cognates with regard to diachronically motivated sound correspondences that appear to facilitate or hinder human performance. as we are interested in inherent intercomprehension, only people who speak their first language (l1) natively and who do not know the stimulus language (lx) have been included in the analysis. after completing a background questionnaire, bulgarian (bg) and russian (ru) participants were asked to translate randomized 120 6 oral 7 bg and ru stimuli into their respective native language in two series of 60 stimuli each. the items were taken from the written intelligibility tests of @xcite in order to obtain a reliable baseline. the number of ru participants is 29, aged between 16 and 48 years (average age 32) 8 with 23 women, 5 men, and 1 not specified. the number of bg participants is 11, aged between 19 and 37 years (average age 27) 9 with 10 females and 1 male . even though these two groups differ considerably in size, statistical analyses based on fewer participants are particularly worthwhile for the practice of foreign language learning and teaching and in experiments involving specific target groups @xcite . in this study, when no statistical observations can be made, we report findings as percentages to indicate success rates based on collected material, bearing in mind that our results should be interpreted with caution as general trends. during the experiments the participants lisin german and english) carried out as challenges in a linguistic game ( https://intercomprehension.coli . ## conclusion and outlook we presented incom.py 2.0, the extended version of the incom.py toolkit for calculating similarities and asymmetries between closely related languages, with a focus on auditory perception. our main conculusions are the following: a closer look at sound correspondences further reveals that a larger number of consonants with a balanced distribution between the two languages also plays a role in spoken word recognition. as a next step, we plan to extend the range of the studied phenomena beyond mere word similarity and to include predictability of constructional similarity by focusing on cross-lingual correspondences of multi-component units in phrasal and sentential contexts. additionally, we plan to integrate reaction times into the experiments in order to study the interaction of linguistic and cognitive aspects of human performance.