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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/2508.14713 |
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| _version_ | 1866918127873294336 |
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| author | Li, Zhipeng Xing, Xiaofen Xing, Jingyuan Hu, Hangrui Lu, Heng Xu, Xiangmin |
| author_facet | Li, Zhipeng Xing, Xiaofen Xing, Jingyuan Hu, Hangrui Lu, Heng Xu, Xiangmin |
| contents | In long-text speech synthesis, current approaches typically convert text to speech at the sentence-level and concatenate the results to form pseudo-paragraph-level speech. These methods overlook the contextual coherence of paragraphs, leading to reduced naturalness and inconsistencies in style and timbre across the long-form speech. To address these issues, we propose a Context-Aware Memory (CAM)-based long-context Text-to-Speech (TTS) model. The CAM block integrates and retrieves both long-term memory and local context details, enabling dynamic memory updates and transfers within long paragraphs to guide sentence-level speech synthesis. Furthermore, the prefix mask enhances the in-context learning ability by enabling bidirectional attention on prefix tokens while maintaining unidirectional generation. Experimental results demonstrate that the proposed method outperforms baseline and state-of-the-art long-context methods in terms of prosody expressiveness, coherence and context inference cost across paragraph-level speech. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Long-Context Speech Synthesis with Context-Aware Memory Li, Zhipeng Xing, Xiaofen Xing, Jingyuan Hu, Hangrui Lu, Heng Xu, Xiangmin Audio and Speech Processing Sound In long-text speech synthesis, current approaches typically convert text to speech at the sentence-level and concatenate the results to form pseudo-paragraph-level speech. These methods overlook the contextual coherence of paragraphs, leading to reduced naturalness and inconsistencies in style and timbre across the long-form speech. To address these issues, we propose a Context-Aware Memory (CAM)-based long-context Text-to-Speech (TTS) model. The CAM block integrates and retrieves both long-term memory and local context details, enabling dynamic memory updates and transfers within long paragraphs to guide sentence-level speech synthesis. Furthermore, the prefix mask enhances the in-context learning ability by enabling bidirectional attention on prefix tokens while maintaining unidirectional generation. Experimental results demonstrate that the proposed method outperforms baseline and state-of-the-art long-context methods in terms of prosody expressiveness, coherence and context inference cost across paragraph-level speech. |
| title | Long-Context Speech Synthesis with Context-Aware Memory |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2508.14713 |