CC-G2PnP: Streaming Grapheme-to-Phoneme and prosody with Conformer-CTC for unsegmented languages
Abstract
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.
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Research summary
The paper presents CC-G2PnP, a novel streaming model for grapheme-to-phoneme and prosody conversion that addresses the challenges of unsegmented languages. Its innovative methodology and robust experimental results position it as a significant contribution to the field of audio processing and speech synthesis.
Methodology
The proposed CC-G2PnP model employs a Conformer-CTC architecture that innovatively processes grapheme tokens in chunks, allowing for streaming inference of phonemic and prosodic labels. The introduction of minimum look-ahead (MLA) is a significant methodological advancement, as it addresses the limitations of previous streaming models that rely on explicit word boundaries. This approach is particularly beneficial for unsegmented languages like Japanese, where word boundaries are not clearly defined. The integration of self-conditioned CTC into the architecture further enhances the model's performance by allowing dynamic learning of alignments between graphemes and phonemes.
Results & evaluation
The experiments conducted on a Japanese dataset demonstrate the effectiveness of CC-G2PnP, showing significant improvements in character error rate (CER) and sentence error rate (SER) compared to baseline models. The use of both objective metrics and subjective assessments of TTS naturalness provides a comprehensive evaluation of the model's performance. The dataset preparation and experimental conditions are well-documented, allowing for a clear understanding of the model's capabilities and limitations.
Reproducibility
While the paper provides detailed descriptions of the model architecture and training procedures, the lack of a publicly available code repository or demo URL limits reproducibility. The absence of specific hyperparameters and training configurations in a readily accessible format could hinder other researchers from replicating the results.
Limitations
One limitation noted is the reliance on a large amount of training data to achieve optimal performance, which may not be feasible for all applications. Additionally, while the model performs well in terms of accuracy, the subjective evaluation of TTS naturalness could vary based on the speaker used during testing, which may not generalize across different voices.
Broader impact
The CC-G2PnP model has the potential to significantly enhance text-to-speech systems, particularly for languages without explicit word boundaries. This could lead to more natural and efficient human-machine interactions in various applications, including virtual assistants, language learning tools, and accessibility technologies for the visually impaired. The advancements in streaming G2PnP could also inspire further research in related areas, such as real-time speech synthesis and multilingual processing. The paper presents CC-G2PnP, a novel streaming model for grapheme-to-phoneme and prosody conversion that addresses the challenges of unsegmented languages. Its innovative methodology and robust experimental results position it as a significant contribution to the field of audio processing and speech synthesis.
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