  This work presents a seemingly simple but effective technique to improve
low-resource ASR systems for phonetic languages. By identifying sets of
acoustically similar graphemes in these languages, we first reduce the output
alphabet of the ASR system using linguistically meaningful reductions and then
reconstruct the original alphabet using a standalone module. We demonstrate
that this lessens the burden and improves the performance of low-resource
end-to-end ASR systems (because only reduced-alphabet predictions are needed)
and that it is possible to design a very simple but effective reconstruction
module that recovers sequences in the original alphabet from sequences in the
reduced alphabet. We present a finite state transducer-based reconstruction
module that operates on the 1-best ASR hypothesis in the reduced alphabet. We
demonstrate the efficacy of our proposed technique using ASR systems for two
Indian languages, Gujarati and Telugu. With access to only 10 hrs of speech
data, we obtain relative WER reductions of up to 7% compared to systems that do
not use any reduction.
