提出流式音素与语调转换模型，适配无词边界日语。
CC-G2PnP: Streaming Grapheme-to-Phoneme and prosody with Conformer-CTC for unsegmented languages
- 基于Conformer-CTC分块处理字符，实现流式音素和语调预测。
- 在无词边界的日语数据上，音素与语调预测准确率显著提升。
- 无需显式词边界，适合无分词语言的语音合成前端。
我们提出CC-G2PnP，一种流式字符转音素和语调（G2PnP）模型，用于以流式方式连接大语言模型与文本到语音系统。该模型基于Conformer-CTC架构，输入字符片段逐块处理，支持音素和语调标签的流式推断。通过保证每个输入字符的最小前瞻尺寸，模型可在每个字符中考虑未来上下文，从而实现稳定的音素与语调标签预测。与依赖显式词边界的传统流式方法不同，CC-G2PnP中的CTC解码器在训练过程中自动学习字符与音素的对齐关系，使其适用于无分词语言。在无明确词边界的日语数据集上的实验表明，该模型在音素与语调标签预测准确性上显著优于基线流式G2PnP模型。
原文摘要 · Abstract (English)
We propose CC-G2PnP, a streaming grapheme-to-phoneme and prosody (G2PnP) model to connect large language model and text-to-speech in a streaming manner. CC-G2PnP is based on Conformer-CTC architecture. Specifically, the input grapheme tokens are processed chunk by chunk, which enables streaming inference of phonemic and prosodic (PnP) labels. By guaranteeing minimal look-ahead size to each input token, the proposed model can consider future context in each token, which leads to stable PnP label prediction. Unlike previous streaming methods that depend on explicit word boundaries, the CTC decoder in CC-G2PnP effectively learns the alignment between graphemes and phonemes during training, making it applicable to unsegmented languages. Experiments on a Japanese dataset, which has no explicit word boundaries, show that CC-G2PnP significantly outperforms the baseline streaming G2PnP model in the accuracy of PnP label prediction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。