  Deep neural networks have been employed for various spoken language
recognition tasks, including tasks that are multilingual by definition such as
spoken language identification. In this paper, we present a neural model for
Slavic language identification in speech signals and analyze its emergent
representations to investigate whether they reflect objective measures of
language relatedness and/or non-linguists' perception of language similarity.
While our analysis shows that the language representation space indeed captures
language relatedness to a great extent, we find perceptual confusability
between languages in our study to be the best predictor of the language
representation similarity.
