# Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models

arXiv: https://arxiv.org/abs/1610.06540 · HTML: https://mlsanity.com/paper/1610.06540/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2016.

## Built on by (most cited first, 10 of 10)

- [ByT5 model for massively multilingual grapheme-to-phoneme conversion](https://mlsanity.com/paper/2204.03067/) (2022)
- [Phoneme-aware Encoding for Prefix-tree-based Contextual ASR](https://mlsanity.com/paper/2312.09582/) (2023)
- [Error-driven Pruning of Language Models for Virtual Assistants](https://mlsanity.com/paper/2102.07219/) (2021)
- [r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation](https://mlsanity.com/paper/2202.11194/) (2022)
- [PhonologyBench: Evaluating Phonological Skills of Large Language Models](https://mlsanity.com/paper/2404.02456/) (2024)
- [Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models](https://mlsanity.com/paper/2201.10716/) (2022)
- [A unified front-end framework for English text-to-speech synthesis](https://mlsanity.com/paper/2305.10666/) (2023)
- [Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings](https://mlsanity.com/paper/2307.16643/) (2023)
- [Helpful Neighbors: Leveraging Neighbors in Geographic Feature Pronunciation](https://mlsanity.com/paper/2210.10200/) (2022)
- [DTW-SiameseNet: Dynamic Time Warped Siamese Network for Mispronunciation Detection and Correction](https://mlsanity.com/paper/2303.00171/) (2023)

## Abstract (arXiv)

We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion. Most previous work has tackled the problem via joint sequence models that require explicit alignments for training. In contrast, the attention-enabled encoder-decoder model allows for jointly learning to align and convert characters to phonemes. We explore different types of attention models, including global and local attention, and our best models achieve state-of-the-art results on three standard data sets (CMUDict, Pronlex, and NetTalk).