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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.17516 |
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| _version_ | 1866911168268861440 |
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| author | Liu, Min Yin, JingJing Zhang, Xiang Hao, Siyu Hu, Yanni Lin, Bin Feng, Yuan Zhou, Hongbin Ye, Jianhao |
| author_facet | Liu, Min Yin, JingJing Zhang, Xiang Hao, Siyu Hu, Yanni Lin, Bin Feng, Yuan Zhou, Hongbin Ye, Jianhao |
| contents | Existing text-to-speech systems predominantly focus on single-sentence synthesis and lack adequate contextual modeling as well as fine-grained performance control capabilities for generating coherent multicast audiobooks. To address these limitations, we propose a context-aware and emotion controllable speech synthesis framework specifically engineered for multicast audiobooks with three key innovations: a context mechanism for contextual consistency, a disentanglement paradigm to decouple style control from speech prompts for semantic consistency, and self-distillation to boost emotional expressiveness and instruction controllability. Experimental results show superior performance across the generation of narration, dialogue, and the whole chapter, significantly outperforming existing baselines. Ablation studies are conducted to validate the effectiveness of our proposed methods. Demo samples can be found in https://everest-ai.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Audiobook-CC: Controllable Long-context Speech Generation for Multicast Audiobook Liu, Min Yin, JingJing Zhang, Xiang Hao, Siyu Hu, Yanni Lin, Bin Feng, Yuan Zhou, Hongbin Ye, Jianhao Audio and Speech Processing Existing text-to-speech systems predominantly focus on single-sentence synthesis and lack adequate contextual modeling as well as fine-grained performance control capabilities for generating coherent multicast audiobooks. To address these limitations, we propose a context-aware and emotion controllable speech synthesis framework specifically engineered for multicast audiobooks with three key innovations: a context mechanism for contextual consistency, a disentanglement paradigm to decouple style control from speech prompts for semantic consistency, and self-distillation to boost emotional expressiveness and instruction controllability. Experimental results show superior performance across the generation of narration, dialogue, and the whole chapter, significantly outperforming existing baselines. Ablation studies are conducted to validate the effectiveness of our proposed methods. Demo samples can be found in https://everest-ai.github.io/. |
| title | Audiobook-CC: Controllable Long-context Speech Generation for Multicast Audiobook |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.17516 |