Quantize More, Lose Less: Autoregressive Generation from Residually Quantized Speech Representations Article Swipe
Yichen Han
,
Xiaoyang Hao
,
Keming Chen
,
Weibo Xiong
,
Jun He
,
Ruonan Zhang
,
Jun Cao
,
Na Li
,
Bowen Li
,
D. M. Zhang
,
Hui Xia
,
Honglei Fu
,
Kai Jia
,
Kaixuan Guo
,
Mingli Jin
,
Qingyun Meng
,
Ruirui Ma
,
Renfei Fang
,
Shaotong Guo
,
Xuhui Li
,
Yang Xiang
,
Ying Zhang
,
Yulong Liu
,
Yunfeng Li
,
Yuyi Zhang
,
Yuze Zhou
,
Z. Jane Wang
,
Zhaowen Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2507.12197
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2507.12197
Text-to-speech (TTS) synthesis has seen renewed progress under the discrete modeling paradigm. Existing autoregressive approaches often rely on single-codebook representations, which suffer from significant information loss. Even with post-hoc refinement techniques such as flow matching, these methods fail to recover fine-grained details (e.g., prosodic nuances, speaker-specific timbres), especially in challenging scenarios like singing voice or music synthesis. We propose QTTS, a novel TTS framework built upon our new audio codec, QDAC. The core innovation of QDAC lies in its end-to-end training of an ASR-based auto-regressive network with a GAN, which achieves superior semantic feature disentanglement for scalable, near-lossless compression. QTTS models these discrete codes using two innovative strategies: the Hierarchical Parallel architecture, which uses a dual-AR structure to model inter-codebook dependencies for higher-quality synthesis, and the Delay Multihead approach, which employs parallelized prediction with a fixed delay to accelerate inference speed. Our experiments demonstrate that the proposed framework achieves higher synthesis quality and better preserves expressive content compared to baseline. This suggests that scaling up compression via multi-codebook modeling is a promising direction for high-fidelity, general-purpose speech and audio generation.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2507.12197
- https://arxiv.org/pdf/2507.12197
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4415142267
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- OpenAlex ID
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https://openalex.org/W4415142267Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2507.12197Digital Object Identifier
- Title
-
Quantize More, Lose Less: Autoregressive Generation from Residually Quantized Speech RepresentationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-16Full publication date if available
- Authors
-
Yichen Han, Xiaoyang Hao, Keming Chen, Weibo Xiong, Jun He, Ruonan Zhang, Jun Cao, Na Li, Bowen Li, D. M. Zhang, Hui Xia, Honglei Fu, Kai Jia, Kaixuan Guo, Mingli Jin, Qingyun Meng, Ruirui Ma, Renfei Fang, Shaotong Guo, Xuhui Li, Yang Xiang, Ying Zhang, Yulong Liu, Yunfeng Li, Yuyi Zhang, Yuze Zhou, Z. Jane Wang, Zhaowen ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2507.12197Publisher landing page
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https://arxiv.org/pdf/2507.12197Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2507.12197Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.discrete | 9, 102 |
| abstract_inverted_index.modeling | 10, 168 |
| abstract_inverted_index.nuances, | 44 |
| abstract_inverted_index.post-hoc | 28 |
| abstract_inverted_index.progress | 6 |
| abstract_inverted_index.proposed | 146 |
| abstract_inverted_index.prosodic | 43 |
| abstract_inverted_index.semantic | 92 |
| abstract_inverted_index.suggests | 161 |
| abstract_inverted_index.superior | 91 |
| abstract_inverted_index.training | 80 |
| abstract_inverted_index.ASR-based | 83 |
| abstract_inverted_index.Multihead | 127 |
| abstract_inverted_index.approach, | 128 |
| abstract_inverted_index.baseline. | 159 |
| abstract_inverted_index.direction | 172 |
| abstract_inverted_index.framework | 63, 147 |
| abstract_inverted_index.inference | 139 |
| abstract_inverted_index.matching, | 34 |
| abstract_inverted_index.paradigm. | 11 |
| abstract_inverted_index.preserves | 154 |
| abstract_inverted_index.promising | 171 |
| abstract_inverted_index.scalable, | 96 |
| abstract_inverted_index.scenarios | 50 |
| abstract_inverted_index.structure | 116 |
| abstract_inverted_index.synthesis | 2, 150 |
| abstract_inverted_index.timbres), | 46 |
| abstract_inverted_index.accelerate | 138 |
| abstract_inverted_index.approaches | 14 |
| abstract_inverted_index.end-to-end | 79 |
| abstract_inverted_index.especially | 47 |
| abstract_inverted_index.expressive | 155 |
| abstract_inverted_index.innovation | 73 |
| abstract_inverted_index.innovative | 106 |
| abstract_inverted_index.prediction | 132 |
| abstract_inverted_index.refinement | 29 |
| abstract_inverted_index.synthesis, | 123 |
| abstract_inverted_index.synthesis. | 56 |
| abstract_inverted_index.techniques | 30 |
| abstract_inverted_index.challenging | 49 |
| abstract_inverted_index.compression | 165 |
| abstract_inverted_index.demonstrate | 143 |
| abstract_inverted_index.experiments | 142 |
| abstract_inverted_index.generation. | 179 |
| abstract_inverted_index.information | 24 |
| abstract_inverted_index.significant | 23 |
| abstract_inverted_index.strategies: | 107 |
| abstract_inverted_index.Hierarchical | 109 |
| abstract_inverted_index.compression. | 98 |
| abstract_inverted_index.dependencies | 120 |
| abstract_inverted_index.fine-grained | 40 |
| abstract_inverted_index.parallelized | 131 |
| abstract_inverted_index.architecture, | 111 |
| abstract_inverted_index.near-lossless | 97 |
| abstract_inverted_index.Text-to-speech | 0 |
| abstract_inverted_index.autoregressive | 13 |
| abstract_inverted_index.high-fidelity, | 174 |
| abstract_inverted_index.higher-quality | 122 |
| abstract_inverted_index.inter-codebook | 119 |
| abstract_inverted_index.multi-codebook | 167 |
| abstract_inverted_index.auto-regressive | 84 |
| abstract_inverted_index.disentanglement | 94 |
| abstract_inverted_index.general-purpose | 175 |
| abstract_inverted_index.single-codebook | 18 |
| abstract_inverted_index.representations, | 19 |
| abstract_inverted_index.speaker-specific | 45 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 28 |
| citation_normalized_percentile |