External Knowledge Augmented Polyphone Disambiguation Using Large
  Language Model
Chen Li
One of the key issues in Mandarin Chinese text-to-speech (TTS) systems is
polyphone disambiguation when doing grapheme-to-phoneme (G2P) conversion. In
this paper, we introduce a novel method to solve the problem as a generation
task. Following the trending research of large language models (LLM) and prompt
learning, the proposed method consists of three modules. Retrieval module
incorporates external knowledge which is a multi-level semantic dictionary of
Chinese polyphonic characters to format the sentence into a prompt. Generation
module adopts the decoder-only Transformer architecture to induce the target
text. Postprocess module corrects the generated text into a valid result if
needed. Experimental results show that our method outperforms the existing
methods on a public dataset called CPP. We also empirically study the impacts
of different templates of the prompt, different sizes of training data, and
whether to incorporate external knowledge.