  Only a handful of the world's languages are abundant with the resources that
enable practical applications of speech processing technologies. One of the
methods to overcome this problem is to use the resources existing in other
languages to train a multilingual automatic speech recognition (ASR) model,
which, intuitively, should learn some universal phonetic representations. In
this work, we focus on gaining a deeper understanding of how general these
representations might be, and how individual phones are getting improved in a
multilingual setting. To that end, we select a phonetically diverse set of
languages, and perform a series of monolingual, multilingual and crosslingual
(zero-shot) experiments. The ASR is trained to recognize the International
Phonetic Alphabet (IPA) token sequences. We observe significant improvements
across all languages in the multilingual setting, and stark degradation in the
crosslingual setting, where the model, among other errors, considers Javanese
as a tone language. Notably, as little as 10 hours of the target language
training data tremendously reduces ASR error rates. Our analysis uncovered that
even the phones that are unique to a single language can benefit greatly from
adding training data from other languages - an encouraging result for the
low-resource speech community.
