  Automated speech recognition coverage of the world's languages continues to
expand. However, standard phoneme based systems require handcrafted lexicons
that are difficult and expensive to obtain. To address this problem, we propose
a training methodology for a grapheme-based speech recognizer that can be
trained in a purely data-driven fashion. Built with LSTM networks and trained
with the cross-entropy loss, the grapheme-output acoustic models we study are
also extremely practical for real-world applications as they can be decoded
with conventional ASR stack components such as language models and FST
decoders, and produce good quality audio-to-grapheme alignments that are useful
in many speech applications. We show that the grapheme models are competitive
in WER with their phoneme-output counterparts when trained on large datasets,
with the advantage that grapheme models do not require explicit linguistic
knowledge as an input. We further compare the alignments generated by the
phoneme and grapheme models to demonstrate the quality of the pronunciations
learnt by them using four Indian languages that vary linguistically in spoken
and written forms.
