# r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation

arXiv: https://arxiv.org/abs/2202.11194 · HTML: https://mlsanity.com/paper/2202.11194/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Transformer based Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/2004.06338/) (2020)
- [Federated Learning with Dynamic Transformer for Text to Speech](https://mlsanity.com/paper/2107.08795/) (2021)
- [Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models](https://mlsanity.com/paper/1610.06540/) (2016)

## Built on by (most cited first, 7 of 7)

- [Pre-Avatar: An Automatic Presentation Generation Framework Leveraging Talking Avatar](https://mlsanity.com/paper/2210.06877/) (2022)
- [Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection](https://mlsanity.com/paper/2205.11738/) (2022)
- [A unified front-end framework for English text-to-speech synthesis](https://mlsanity.com/paper/2305.10666/) (2023)
- [Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition](https://mlsanity.com/paper/2209.15176/) (2022)
- [QSpeech: Low-Qubit Quantum Speech Application Toolkit](https://mlsanity.com/paper/2205.13221/) (2022)
- [Learning Invariant Representation and Risk Minimized for Unsupervised Accent Domain Adaptation](https://mlsanity.com/paper/2210.08182/) (2022)
- [Phonemizing User-Generated Text: A Benchmark, Taxonomy, and Compositional Approach](https://mlsanity.com/paper/2609.27205/) (2026)

## Abstract (arXiv)

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).