Most Chinese Grapheme-to-Phoneme (G2P) systems employ a three-stage framework
that first transforms input sequences into character embeddings, obtains
linguistic information using language models, and then predicts the phonemes
based on global context about the entire input sequence. However, linguistic
knowledge alone is often inadequate. Language models frequently encode overly
general structures of a sentence and fail to cover specific cases needed to use
phonetic knowledge. Also, a handcrafted post-processing system is needed to
address the problems relevant to the tone of the characters. However, the
system exhibits inconsistency in the segmentation of word boundaries which
consequently degrades the performance of the G2P system. To address these
issues, we propose the Reinforcer that provides strong inductive bias for
language models by emphasizing the phonological information between neighboring
characters to help disambiguate pronunciations. Experimental results show that
the Reinforcer boosts the cutting-edge architectures by a large margin. We also
combine the Reinforcer with a large-scale pre-trained model and demonstrate the
validity of using neighboring context in knowledge transfer scenarios.

本研究提出了 Reinforcer 方法，通过强化邻近字符间的语音信息，为语言模型提供强有力的归纳偏置，帮助消除发音的歧义，实验证明该方法大幅提升了最先进的语音转文字架构的性能，并将其与大规模预先训练的模型结合使用，证明了在知识转移场景中使用邻近上下文的有效性。

中文字素转音素只需好邻居

Good Neighbors Are All You Need for Chinese Grapheme-to-Phoneme Conversion

Human infants acquire their verbal lexicon from minimal prior knowledge of
language based on the statistical properties of phonological distributions and
the co-occurrence of other sensory stimuli. In this study, we propose a novel
fully unsupervised learning method discovering speech units by utilizing
phonological information as a distributional cue and object information as a
co-occurrence cue. The proposed method can not only (1) acquire words and
phonemes from speech signals using unsupervised learning, but can also (2)
utilize object information based on multiple modalities (i.e., vision, tactile,
and auditory) simultaneously. The proposed method is based on the Nonparametric
Bayesian Double Articulation Analyzer (NPB-DAA) discovering phonemes and words
from phonological features, and Multimodal Latent Dirichlet Allocation (MLDA)
categorizing multimodal information obtained from objects. In the experiment,
the proposed method showed higher word discovery performance than the baseline
methods. In particular, words that expressed the characteristics of the object
(i.e., words corresponding to nouns and adjectives) were segmented accurately.
Furthermore, we examined how learning performance is affected by differences in
the importance of linguistic information. When the weight of the word modality
was increased, the performance was further improved compared to the fixed
condition.

通过利用音韵信息和对象信息来发现言语单元的全新无监督学习方法，能够同时根据多种模态（即视觉、触觉和听觉）使用对象信息，该方法基于非参数贝叶斯双连音分析器（NPB-DAA）发现音素和单词以及多模态潜在狄利克雷分配（MLDA）分类来自对象的多模态信息，实验表明，所提出的方法比现有基准方法具有更高的词汇发现性能。