Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis
arXiv:2606.25369v1 Announce Type: cross Abstract: While large language model (LLM)-based text-to-speech (TTS) systems have achieved high-quality speech synthesis, most existing systems focus on English and Chinese. Japanese, however, remains under-explored, and its unique linguistic challenges, such as widespread context-dependent kanji polyphony, have yet to be adequately tackled. Here we introduce Sarashina2.2-TTS (https://github.com/sbintuitions/sarashina2.2-tts), a Japanese-centric LLM-TTS system that tackles these challenges through a dual approach: data strategy and evaluation methodolog
The proliferation of LLM-based TTS systems highlights the need to address language-specific challenges, particularly for complex languages like Japanese that have been less explored.
This development addresses a key linguistic barrier for Japanese in advanced AI speech generation, potentially accelerating its integration into various applications and enhancing user experience.
Japanese TTS systems will achieve higher quality and accuracy in handling phonetic complexities, making LLM-driven voice interfaces more viable for the Japanese market.
- · Japanese AI developers
- · Japanese tech users
- · Multilingual LLM-TTS platforms
- · AI localization services
- · Monolingual English/Chinese TTS focus
- · Low-quality Japanese TTS providers
Improved Japanese text-to-speech quality for diverse applications like customer service and entertainment.
Increased adoption of AI voice assistants and interfaces within Japan, potentially boosting digital literacy among demographics less comfortable with text input.
Enhanced cultural dissemination of Japanese media and content globally through high-fidelity, nuanced AI-generated speech.
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Read at arXiv cs.CL