CC-G2PnP: Revolutionizing Speech Synthesis with Streaming AI for Unsegmented Languages
research#voice🔬 Research|Analyzed: Feb 20, 2026 05:03•
Published: Feb 20, 2026 05:00
•1 min read
•ArXiv Audio SpeechAnalysis
CC-G2PnP is an exciting new model that seamlessly connects Generative AI with text-to-speech capabilities. The Conformer-CTC architecture allows for real-time processing of graphemes, enabling a streaming approach to phoneme and prosody prediction. This advancement promises more natural and efficient speech synthesis, especially for languages without clear word boundaries like Japanese.
Key Takeaways
- •CC-G2PnP uses a streaming approach to connect Large Language Model and text-to-speech.
- •The model employs a Conformer-CTC architecture for efficient grapheme processing.
- •It excels in languages like Japanese that lack explicit word boundaries.
Reference / Citation
View Original"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."