The Grapheme-to-Phoneme (G2P) task aims to convert orthographic input into a
discrete phonetic representation. G2P conversion is beneficial to various
speech processing applications, such as text-to-speech and speech recognition.
However, these tend to rely on manually-annotated pronunciation dictionaries,
which are often time-consuming and costly to acquire. In this paper, we propose
a method to improve the G2P conversion task by learning pronunciation examples
from audio recordings. Our approach bootstraps a G2P with a small set of
annotated examples. The G2P model is used to train a multilingual phone
recognition system, which then decodes speech recordings with a phonetic
representation. Given hypothesized phoneme labels, we learn pronunciation
dictionaries for out-of-vocabulary words, and we use those to re-train the G2P
system. Results indicate that our approach consistently improves the phone
error rate of G2P systems across languages and amount of available data.

通过从音频记录中学习发音示例来改善 G2P 转换任务的方法，利用少量带有注释的示例训练 G2P 模型，使用多语言电话识别系统进行解码，学习对于未收录词汇的发音字典，并重新训练 G2P 系统，该方法在不同语言和数据量的情况下持续提高 G2P 系统的音素错误率。

通过学习语音录音中的发音来改进字素转音元

Improving grapheme-to-phoneme conversion by learning pronunciations from  speech recordings

In this paper, we investigate the application of end-to-end and multi-module
frameworks for G2P conversion for the Persian language. The results demonstrate
that our proposed multi-module G2P system outperforms our end-to-end systems in
terms of accuracy and speed. The system consists of a pronunciation dictionary
as our look-up table, along with separate models to handle homographs, OOVs and
ezafe in Persian created using GRU and Transformer architectures. The system is
sequence-level rather than word-level, which allows it to effectively capture
the unwritten relations between words (cross-word information) necessary for
homograph disambiguation and ezafe recognition without the need for any
pre-processing. After evaluation, our system achieved a 94.48% word-level
accuracy, outperforming the previous G2P systems for Persian.

本研究探讨了针对波斯语的 G2P 转换的端到端和多模块框架的应用，结果表明，我们的多模块 G2P 系统在准确性和速度方面优于我们的端到端系统。该系统采用发音字典作为我们的查找表，并使用 GRU 和 Transformer 架构创建用于处理 Persian 的同音异型体，OOVs 和 ezafe 的独立模型。经过评估，我们的系统在单词级准确率上达到了 94.48％，优于以前的波斯语 G2P 系统。

针对波斯语单词间关系的多模块 G2P 转换器

Multi-Module G2P Converter for Persian Focusing on Relations between Words

Attention mechanism is one of the most successful techniques in deep learning
based Natural Language Processing (NLP). The transformer network architecture
is completely based on attention mechanisms, and it outperforms
sequence-to-sequence models in neural machine translation without recurrent and
convolutional layers. Grapheme-to-phoneme (G2P) conversion is a task of
converting letters (grapheme sequence) to their pronunciations (phoneme
sequence). It plays a significant role in text-to-speech (TTS) and automatic
speech recognition (ASR) systems. In this paper, we investigate the application
of transformer architecture to G2P conversion and compare its performance with
recurrent and convolutional neural network based approaches. Phoneme and word
error rates are evaluated on the CMUDict dataset for US English and the NetTalk
dataset. The results show that transformer based G2P outperforms the
convolutional-based approach in terms of word error rate and our results
significantly exceeded previous recurrent approaches (without attention)
regarding word and phoneme error rates on both datasets. Furthermore, the size
of the proposed model is much smaller than the size of the previous approaches.

本文研究将 transformer 架构应用于 G2P 转换，并将其性能与循环卷积神经网络等之前的方法进行比较。结果显示，基于 transformer 的 G2P 在单词错误率方面优于基于卷积的方法，并且我们的结果在两个数据集上的单词和音素错误率方面显着超过之前的循环方法（无注意力）。此外，所提出的模型的大小要小得多。