  Multilingual training is effective in improving low-resource ASR, which may
partially be explained by phonetic representation sharing between languages. In
end-to-end (E2E) ASR systems, graphemes are often used as basic modeling units,
however graphemes may not be ideal for multilingual phonetic sharing. In this
paper, we leverage International Phonetic Alphabet (IPA) based
language-universal phonetic model to improve low-resource ASR performances, for
the first time within the attention encoder-decoder architecture. We propose an
adaptation method on the phonetic IPA model to further improve the proposed
approach on extreme low-resource languages. Experiments carried out on the
open-source MLS corpus and our internal databases show our approach outperforms
baseline monolingual models and most state-of-the-art works. Our main approach
and adaptation are effective on extremely low-resource languages, even within
domain- and language-mismatched scenarios.
