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.

翻译：这项工作提出了一种似乎简单但有效的技术,用于改进低资源语言的语音语言ASR系统。通过在这些语言中找出几组声学上相似的图形,我们首先使用语言上有意义的减少来减少ASR系统的输出字母,然后使用一个独立的模块来重建原字母。我们证明,这减轻了低资源终端到终端的ASR系统的负担,并改进了低资源终端到终端的功能(因为只需要减少字母的预测),并且有可能设计一个非常简单而有效的重建模块,从减少字母的顺序中恢复原字母序列的顺序。我们提出了一个基于有限的基于传输器的重建模块,在减少字母中的1个最佳ASR假设上运作。我们用ASR系统展示了我们提议的印度两种语言(古吉拉特语和泰卢古语)使用ASR系统的效率。我们只获得10个小时的语音数据,因此我们相对减少了7%的WER,而没有使用任何缩减的系统。