We propose two models for verbalizing numbers, a key component in speech
recognition and synthesis systems. The first model uses an end-to-end recurrent
neural network. The second model, drawing inspiration from the linguistics
literature, uses finite-state transducers constructed with a minimal amount of
training data. While both models achieve near-perfect performance, the latter model
can be trained using several orders of magnitude less data than the former, making
it particularly useful for low-resource languages.