End-to-end automatic speech recognition (ASR) systems are increasingly being favoured due to their direct treatment of the problem of speech to text conversion. However, these systems are known to be data hungry and hence underperform in low-resource settings. In this work , we propose a seemingly simple but effective technique to improve low-resource end-to-end ASR performance. We compress the output vocabulary of the end-to-end ASR  system using linguistically meaningful reductions and then reconstruct the original vocabulary using a standalone module. Our objective is two-fold: to lessen the burden on the low-resource end-to-end ASR system by reducing the output vocabulary space and to design a powerful reconstruction module that recovers sequences in the original vocabulary from sequences in the reduced vocabulary . We present two reconstruction modules, an encoder decoder-based architecture and a finite state transducer-based model . We demonstrate the efficacy of our proposed techniques using ASR systems for two Indian languages, Gujarati and Telugu  .
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 .
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End-to-end automatic speech recognition (ASR) systems are increasingly being favoured due to their direct treatment of the problem of speech to text conversion. However, these systems are known to be data hungry and hence underperform in low-resource settings. In this work , we propose
This work presents
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end-to-end ASR performance. We compress the output vocabulary of the end-to-end
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systems for phonetic languages. By identifying sets of acoustically similar graphemes in these languages, we first reduce the output alphabet of the ASR
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vocabulary
alphabet
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Our objective is two-fold: to lessen the burden on the
We demonstrate that this lessens the burden and improves the performance of
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system by reducing the output vocabulary space and
systems (because only reduced-alphabet predictions are needed) and that it is possible
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powerful
very simple but effective
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vocabulary
alphabet
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vocabulary
alphabet
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two reconstruction modules, an encoder decoder-based architecture and
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model
reconstruction module that operates on the 1-best ASR hypothesis in the reduced alphabet
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techniques
technique
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. 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