In Linguistics, a grapheme is a written unit of a writing system
corresponding to a phonological sound. In Natural Language Processing tasks,
written language is analysed through two different mediums, word analysis, and
character analysis. This paper focuses on a third approach, the analysis of
graphemes. Graphemes have advantages over word and character analysis by being
self-contained representations of phonetic sounds. Due to the nature of
splitting a word into graphemes being based on complex, non-binary rules, the
application of fuzzy logic would provide a suitable medium upon which to
predict the number of graphemes in a word. This paper proposes the application
of a Fuzzy Inference System to split words into their graphemes. This Fuzzy
Inference System results in a correct prediction of the number of graphemes in
a word 50.18% of the time, with 93.51% being within a margin of +- 1 from the
correct classification. Given the variety in language, graphemes are tied with
pronunciation and therefore can change depending on a regional accent/dialect,
the +- 1 accuracy represents the impreciseness of grapheme classification when
regional variances are accounted for. To give a baseline of comparison, a
second method involving a recursive IPA mapping exercise using a pronunciation
dictionary was developed to allow for comparisons to be made.

该论文提出了一种使用模糊推理系统将词语分解为字素的方法，其中字素是语音的自包含表示形式。该系统可正确预测词语中字素的数量，50.18% 的时间内准确率高于正确分类的容差范围内的 93.51%，但这种准确率会受区域口音和方言的影响。该论文还提供了另一种基准方法，通过递归的语音国际音标映射和发音字典的使用进行比较。

通过模糊推理系统对英语单词中的字素进行分类

Classifying Graphemes in English Words Through the Application of a  Fuzzy Inference System

This paper introduces PnG BERT, a new encoder model for neural TTS. This
model is augmented from the original BERT model, by taking both phoneme and
grapheme representations of text as input, as well as the word-level alignment
between them. It can be pre-trained on a large text corpus in a self-supervised
manner, and fine-tuned in a TTS task. Experimental results show that a neural
TTS model using a pre-trained PnG BERT as its encoder yields more natural
prosody and more accurate pronunciation than a baseline model using only
phoneme input with no pre-training. Subjective side-by-side preference
evaluations show that raters have no statistically significant preference
between the speech synthesized using a PnG BERT and ground truth recordings
from professional speakers.

介绍了 PnG BERT，它是一种新的神经 TTS 编码器模型，使用输入的音素和字素表示以及它们之间的单词级对齐来增强原始的 BERT 模型。该模型可以进行自我监督的大型文本语料库的预训练，并通过 TTS 任务进行微调。实验结果表明，使用 PnG BERT 作为编码器进行预训练的神经 TTS 模型产生的语音韵律更自然，发音更准确，比仅使用音素输入而不预训练的基线模型表现更好。主观音频偏好评估表明，听者对使用 PnG BERT 合成的语音和专业演讲者的真实录音之间没有显着的统计差异。