  Grapheme-based acoustic modeling has recently been shown to outperform
phoneme-based approaches in both hybrid and end-to-end automatic speech
recognition (ASR), even on non-phonemic languages like English. However,
graphemic ASR still has problems with rare long-tail words that do not follow
the standard spelling conventions seen in training, such as entity names. In
this work, we present a novel method to train a statistical
grapheme-to-grapheme (G2G) model on text-to-speech data that can rewrite an
arbitrary character sequence into more phonetically consistent forms. We show
that using G2G to provide alternative pronunciations during decoding reduces
Word Error Rate by 3% to 11% relative over a strong graphemic baseline and
bridges the gap on rare name recognition with an equivalent phonetic setup.
Unlike many previously proposed methods, our method does not require any change
to the acoustic model training procedure. This work reaffirms the efficacy of
grapheme-based modeling and shows that specialized linguistic knowledge, when
available, can be leveraged to improve graphemic ASR.
