  While speech recognition has seen a surge in interest and research over the
last decade, most machine learning models for speech recognition either require
large training datasets or lots of storage and memory. Combined with the
prominence of English as the number one language in which audio data is
available, this means most other languages currently lack good speech
recognition models.
  The method presented in this paper shows how to create and train models for
speech recognition in any language which are not only highly accurate, but also
require very little storage, memory and training data when compared with
traditional models. This allows training models to recognize any language and
deploying them on edge devices such as mobile phones or car displays for fast
real-time speech recognition.
