{"ID":6023482,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T09:20:07.340435153Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06054","arxiv_id":"2607.06054","title":"BlueMagpie-TTS: A Token-Efficient Tokenizer, Language Model, and TTS for Taiwanese-Accent Code-Switching Speech","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/character) with the smallest vocabulary among nine tokenizers. Barbet, a billion-parameter Traditional-Chinese language model trained on PangolinTokenizer, serves as the text-semantic frontend and ranks first among comparable public models on a 14-task evaluation. BlueMagpie-TTS attaches Barbet to the pretrained acoustic stack of VoxCPM2 through a learned bridge, keeping the acoustic stack fixed. On a 1000-sentence Taiwan-localized test set, it lowers CER from 11.45% to 4.81% and WER from 14.83% to 5.36%, relative reductions of 58.0% and 63.9%. In a blind listening study on 500 of these sentences with ten listeners, 65.6% of majority votes prefer BlueMagpie-TTS.","short_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...","url_abs":"https://arxiv.org/abs/2607.06054","url_pdf":"https://arxiv.org/pdf/2607.06054v1","authors":"[\"Ho Lam Chung\",\"Bo-Xuan Zheng\",\"Cheng-Chieh Huang\",\"Cheng-Han Chang\",\"Jung-Ching Chen\",\"Lok-Lam Ieong\",\"Ting-Lin Hsiao\",\"Yu-Cheng Lee\",\"Yi-Hsin Chung\",\"Yu-Kai Guo\",\"Hung-yi Lee\"]","published":"2026-07-07T09:31:19Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}