From `Snippet-lects' to Doculects and Dialects: Leveraging Neural
  Representations of Speech for Placing Audio Signals in a Language Landscape
Séverine Guillaume Guillaume Wisniewski Alexis Michaud
XLSR-53 a multilingual model of speech, builds a vector representation from
audio, which allows for a range of computational treatments. The experiments
reported here use this neural representation to estimate the degree of
closeness between audio files, ultimately aiming to extract relevant linguistic
properties. We use max-pooling to aggregate the neural representations from a
"snippet-lect" (the speech in a 5-second audio snippet) to a "doculect" (the
speech in a given resource), then to dialects and languages. We use data from
corpora of 11 dialects belonging to 5 less-studied languages. Similarity
measurements between the 11 corpora bring out greatest closeness between those
that are known to be dialects of the same language. The findings suggest that
(i) dialect/language can emerge among the various parameters characterizing
audio files and (ii) estimates of overall phonetic/phonological closeness can
be obtained for a little-resourced or fully unknown language. The findings help
shed light on the type of information captured by neural representations of
speech and how it can be extracted from these representations