BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone Disambiguation -- Challenges and Insights
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910804846051328 |
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| author | Hsu, Chan-Jan Lin, Yi-Cheng Lin, Chia-Chun Chen, Wei-Chih Chung, Ho Lam Li, Chen-An Chen, Yi-Chang Yu, Chien-Yu Lee, Ming-Ji Chen, Chien-Cheng Huang, Ru-Heng Lee, Hung-yi Shiu, Da-Shan |
| author_facet | Hsu, Chan-Jan Lin, Yi-Cheng Lin, Chia-Chun Chen, Wei-Chih Chung, Ho Lam Li, Chen-An Chen, Yi-Chang Yu, Chien-Yu Lee, Ming-Ji Chen, Chien-Cheng Huang, Ru-Heng Lee, Hung-yi Shiu, Da-Shan |
| contents | We present BreezyVoice, a Text-to-Speech (TTS) system specifically adapted for Taiwanese Mandarin, highlighting phonetic control abilities to address the unique challenges of polyphone disambiguation in the language. Building upon CosyVoice, we incorporate a $S^{3}$ tokenizer, a large language model (LLM), an optimal-transport conditional flow matching model (OT-CFM), and a grapheme to phoneme prediction model, to generate realistic speech that closely mimics human utterances. Our evaluation demonstrates BreezyVoice's superior performance in both general and code-switching contexts, highlighting its robustness and effectiveness in generating high-fidelity speech. Additionally, we address the challenges of generalizability in modeling long-tail speakers and polyphone disambiguation. Our approach significantly enhances performance and offers valuable insights into the workings of neural codec TTS systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17790 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone Disambiguation -- Challenges and Insights Hsu, Chan-Jan Lin, Yi-Cheng Lin, Chia-Chun Chen, Wei-Chih Chung, Ho Lam Li, Chen-An Chen, Yi-Chang Yu, Chien-Yu Lee, Ming-Ji Chen, Chien-Cheng Huang, Ru-Heng Lee, Hung-yi Shiu, Da-Shan Computation and Language Artificial Intelligence We present BreezyVoice, a Text-to-Speech (TTS) system specifically adapted for Taiwanese Mandarin, highlighting phonetic control abilities to address the unique challenges of polyphone disambiguation in the language. Building upon CosyVoice, we incorporate a $S^{3}$ tokenizer, a large language model (LLM), an optimal-transport conditional flow matching model (OT-CFM), and a grapheme to phoneme prediction model, to generate realistic speech that closely mimics human utterances. Our evaluation demonstrates BreezyVoice's superior performance in both general and code-switching contexts, highlighting its robustness and effectiveness in generating high-fidelity speech. Additionally, we address the challenges of generalizability in modeling long-tail speakers and polyphone disambiguation. Our approach significantly enhances performance and offers valuable insights into the workings of neural codec TTS systems. |
| title | BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone Disambiguation -- Challenges and Insights |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2501.17790 |