MOSS-TTSD: Text to Spoken Dialogue Generation
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| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911531334107136 |
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| author | Zhang, Yuqian Yu, Donghua Lin, Zhengyuan Jiang, Botian Chen, Mingshu Jiang, Yaozhou Zhao, Yiwei Zhang, Yiyang Yuan, Yucheng Chen, Hanfu Huang, Kexin Zhan, Jun Chang, Cheng Fei, Zhaoye Li, Shimin Yang, Xiaogui Cheng, Qinyuan Qiu, Xipeng |
| author_facet | Zhang, Yuqian Yu, Donghua Lin, Zhengyuan Jiang, Botian Chen, Mingshu Jiang, Yaozhou Zhao, Yiwei Zhang, Yiyang Yuan, Yucheng Chen, Hanfu Huang, Kexin Zhan, Jun Chang, Cheng Fei, Zhaoye Li, Shimin Yang, Xiaogui Cheng, Qinyuan Qiu, Xipeng |
| contents | Spoken dialogue generation is crucial for applications like podcasts, dynamic commentary, and entertainment content, but poses significant challenges compared to single-utterance text-to-speech (TTS). Key requirements include accurate turn-taking, cross-turn acoustic consistency, and long-form stability, which current models often fail to address due to a lack of dialogue context modeling. To bridge this gap, we present MOSS-TTSD, a spoken dialogue synthesis model designed for expressive, multi-party conversational speech across multiple languages. With enhanced long-context modeling, MOSS-TTSD generates long-form spoken conversations from dialogue scripts with explicit speaker tags, supporting up to 60 minutes of single-pass synthesis, multi-party dialogue with up to 5 speakers, and zero-shot voice cloning from a short reference audio clip. The model supports various mainstream languages, including English and Chinese, and is adapted to several long-form scenarios. Additionally, to address limitations of existing evaluation methods, we propose TTSD-eval, an objective evaluation framework based on forced alignment that measures speaker attribution accuracy and speaker similarity without relying on speaker diarization tools. Both objective and subjective evaluation results show that MOSS-TTSD surpasses strong open-source and proprietary baselines in dialogue synthesis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19739 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MOSS-TTSD: Text to Spoken Dialogue Generation Zhang, Yuqian Yu, Donghua Lin, Zhengyuan Jiang, Botian Chen, Mingshu Jiang, Yaozhou Zhao, Yiwei Zhang, Yiyang Yuan, Yucheng Chen, Hanfu Huang, Kexin Zhan, Jun Chang, Cheng Fei, Zhaoye Li, Shimin Yang, Xiaogui Cheng, Qinyuan Qiu, Xipeng Sound Artificial Intelligence Computation and Language Spoken dialogue generation is crucial for applications like podcasts, dynamic commentary, and entertainment content, but poses significant challenges compared to single-utterance text-to-speech (TTS). Key requirements include accurate turn-taking, cross-turn acoustic consistency, and long-form stability, which current models often fail to address due to a lack of dialogue context modeling. To bridge this gap, we present MOSS-TTSD, a spoken dialogue synthesis model designed for expressive, multi-party conversational speech across multiple languages. With enhanced long-context modeling, MOSS-TTSD generates long-form spoken conversations from dialogue scripts with explicit speaker tags, supporting up to 60 minutes of single-pass synthesis, multi-party dialogue with up to 5 speakers, and zero-shot voice cloning from a short reference audio clip. The model supports various mainstream languages, including English and Chinese, and is adapted to several long-form scenarios. Additionally, to address limitations of existing evaluation methods, we propose TTSD-eval, an objective evaluation framework based on forced alignment that measures speaker attribution accuracy and speaker similarity without relying on speaker diarization tools. Both objective and subjective evaluation results show that MOSS-TTSD surpasses strong open-source and proprietary baselines in dialogue synthesis. |
| title | MOSS-TTSD: Text to Spoken Dialogue Generation |
| topic | Sound Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2603.19739 |