量化更多，损失更少：基于残差量化语音表示的自回归生成
Quantize More, Lose Less: Autoregressive Generation from Residually Quantized Speech Representations
AI总结：
提出基于残差量化音频编解码器QDAC的TTS框架QTTS，通过分层并行和延迟多头策略实现多码本自回归建模，有效提升合成质量与推理速度。
摘要：
文本到语音（TTS）合成在离散建模范式下取得了新的进展。现有的自回归方法通常依赖单码本表示，这会导致严重的信息丢失。即使采用流匹配等事后细化技术，这些方法也无法恢复细粒度细节（例如韵律细微差别、特定说话人的音色），特别是在歌唱声音或音乐合成等具有挑战性的场景中。我们提出了QTTS，这是一种基于我们新音频编解码器QDAC构建的新型TTS框架。QDAC的核心创新在于利用GAN对基于ASR的自回归网络进行端到端训练，实现了卓越的语义特征解耦，从而支持可扩展的近无损压缩。QTTS使用两种创新策略对这些离散码进行建模：一是分层并行架构，该架构使用双AR结构对码本间的依赖关系进行建模以实现更高质量的合成；二是延迟多头方法，该方法采用带有固定延迟的并行化预测来加速推理速度。我们的实验表明，与基线相比，所提出的框架实现了更高的合成质量，并更好地保留了表现力内容。这表明通过多码本建模扩大压缩规模，是实现高保真、通用语音和音频生成的一个有前景的方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
