  Building language-universal speech recognition systems entails producing
phonological units of spoken sound that can be shared across languages. While
speech annotations at the language-specific phoneme or surface levels are
readily available, annotations at a universal phone level are relatively rare
and difficult to produce. In this work, we present a general framework to
derive phone-level supervision from only phonemic transcriptions and
phone-to-phoneme mappings with learnable weights represented using weighted
finite-state transducers, which we call differentiable allophone graphs. By
training multilingually, we build a universal phone-based speech recognition
model with interpretable probabilistic phone-to-phoneme mappings for each
language. These phone-based systems with learned allophone graphs can be used
by linguists to document new languages, build phone-based lexicons that capture
rich pronunciation variations, and re-evaluate the allophone mappings of seen
language. We demonstrate the aforementioned benefits of our proposed framework
with a system trained on 7 diverse languages.
