According to the Language Familiarity Effect (LFE), people are better at
discriminating between speakers of their native language. Although this
cognitive effect was largely studied in the literature, experiments have only
been conducted on a limited number of language pairs and their results only
show the presence of the effect without yielding a gradual measure that may
vary across language pairs. In this work, we show that the computational model
of LFE introduced by Thorburn, Feldmand and Schatz (2019) can address these two
limitations. In a first experiment, we attest to this model's capacity to
obtain a gradual measure of the LFE by replicating behavioural findings on
native and accented speech. In a second experiment, we evaluate LFE on a large
number of language pairs, including many which have never been tested on
humans. We show that the effect is replicated across a wide array of languages,
providing further evidence of its universality. Building on the gradual measure
of LFE, we also show that languages belonging to the same family yield smaller
scores, supporting the idea of an effect of language distance on LFE.

该研究提出一种计算模型来研究母语者较易发现其母语的讲话者的语言熟悉度效应，并发现语言家族间的语言距离会对该效应产生影响。