Text-to-Speech synthesis in Indian languages has a seen lot of progress over
the decade partly due to the annual Blizzard challenges. These systems assume
the text to be written in Devanagari or Dravidian scripts which are nearly
phonemic orthography scripts. However, the most common form of computer
interaction among Indians is ASCII written transliterated text. Such text is
generally noisy with many variations in spelling for the same word. In this
paper we evaluate three approaches to synthesize speech from such noisy ASCII
text: a naive Uni-Grapheme approach, a Multi-Grapheme approach, and a
supervised Grapheme-to-Phoneme (G2P) approach. These methods first convert the
ASCII text to a phonetic script, and then learn a Deep Neural Network to
synthesize speech from that. We train and test our models on Blizzard Challenge
datasets that were transliterated to ASCII using crowdsourcing. Our experiments
on Hindi, Tamil and Telugu demonstrate that our models generate speech of
competetive quality from ASCII text compared to the speech synthesized from the
native scripts. All the accompanying transliterated datasets are released for
public access.Comment: 6 pages, 5 figures -- Accepted in 9th ISCA Speech Synthesis Worksho