  Grapheme-to-phoneme (G2P) conversion is the process of converting the written
form of words to their pronunciations. It has an important role for
text-to-speech (TTS) synthesis and automatic speech recognition (ASR) systems.
In this paper, we aim to evaluate and enhance the robustness of G2P models. We
show that neural G2P models are extremely sensitive to orthographical
variations in graphemes like spelling mistakes. To solve this problem, we
propose three controlled noise introducing methods to synthesize noisy training
data. Moreover, we incorporate the contextual information with the baseline and
propose a robust training strategy to stabilize the training process. The
experimental results demonstrate that our proposed robust G2P model (r-G2P)
outperforms the baseline significantly (-2.73\% WER on Dict-based benchmarks
and -9.09\% WER on Real-world sources).
