  State-of-the-art automatic speech recognition (ASR) systems struggle with the
lack of data for rare accents. For sufficiently large datasets, neural engines
tend to outshine statistical models in most natural language processing
problems. However, a speech accent remains a challenge for both approaches.
Phonologists manually create general rules describing a speaker's accent, but
their results remain underutilized. In this paper, we propose a model that
automatically retrieves phonological generalizations from a small dataset. This
method leverages the difference in pronunciation between a particular dialect
and General American English (GAE) and creates new accented samples of words.
The proposed model is able to learn all generalizations that previously were
manually obtained by phonologists. We use this statistical method to generate a
million phonological variations of words from the CMU Pronouncing Dictionary
and train a sequence-to-sequence RNN to recognize accented words with 59%
accuracy.
