What do self-supervised speech models know about Dutch? Analyzing
  advantages of language-specific pre-training
Authors: name: Marianne de Heer Kloots, name: Hosein Mohebbi, name: Charlotte Pouw, name: Gaofei Shen, name: Willem Zuidema, name: Martijn Bentum

How language-specific are speech representations learned by self-supervised
models? Existing work has shown that a range of linguistic features can be
successfully decoded from end-to-end models trained only on speech recordings.
However, it's less clear to what extent pre-training on specific languages
improves language-specific linguistic information. Here we test the encoding of
Dutch phonetic and lexical information in internal representations of
self-supervised Wav2Vec2 models. Pre-training exclusively on Dutch improves the
representation of Dutch linguistic features as compared to pre-training on
similar amounts of English or larger amounts of multilingual data. This
language-specific advantage is well-detected by trained clustering or
classification probes, and partially observable using zero-shot metrics.
Furthermore, the language-specific benefit on linguistic feature encoding
aligns with downstream performance on Automatic Speech Recognition.