BlueMagpie-TTS: A Token-Efficient Tokenizer, Language Model, and TTS for Taiwanese-Accent Code-Switching Speech
arXiv:2607.06054v1 Announce Type: cross Abstract: Off-the-shelf TTS systems are poorly adapted to Taiwanese Mandarin. Their accent defaults to other Mandarin variants, their tokenizers over-segment common Taiwanese text, and their pronunciation degrades at code-switching boundaries where Chinese and English alternate within one utterance. These problems share one root: the text side lacks adaptation to the Taiwanese context. We address the text side from the bottom up. PangolinTokenizer, a byte-level BPE tokenizer trained on Taiwan-context data, reaches the lowest token rate (0.485 tokens/char
The development of regionally specific AI models is accelerating as general-purpose models struggle with nuanced local linguistic and cultural contexts, especially for code-switching in speech.
This development highlights the growing strategic importance of localized AI, particularly in sensitive linguistic contexts like Taiwanese Mandarin, which can underpin national digital sovereignty efforts.
The ability to develop high-quality, regionally tailored TTS systems with efficient tokenization and language models directly improves the utility and adoption of AI in specific linguistic markets.
- · Taiwanese tech sector
- · Localized AI development teams
- · Digital content creators targeting Taiwan
- · Language-specific datasets and models
- · Generic, untailored TTS providers
- · US/China AI stacks lacking regional specialization
Improved digital accessibility and user experience for Taiwanese Mandarin speakers.
Increased demand for region-specific data and AI talent to build competitive localized models.
Potential for other nations or regions with unique linguistic challenges to pursue similar sovereign AI development strategies.
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Read at arXiv cs.CL