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

Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification