自回归语音合成中解码顺序至关重要
Decoding Order Matters in Autoregressive Speech Synthesis
AI总结：
该研究发现自回归语音合成中解码顺序对语音质量有显著影响，自适应解码优于固定顺序方法，且1位量化可支持高质量语音生成。
摘要：
自回归语音合成通常采用从左到右的顺序，但生成顺序是一个建模选择。我们通过掩码扩散框架研究解码顺序，该框架逐步解掩码位置并在训练和推理中允许任意解码顺序。通过插值身份和随机排列之间，我们证明了解码顺序中的随机性会影响语音质量。我们进一步比较了固定策略，如l2r和r2l，与自适应策略，如Top-K，发现固定顺序解码，包括主导的从左到右方法，是次优的，而自适应解码产生更好的性能。最后，由于掩码扩散需要离散输入，我们量化了声学表示，并发现即使1位量化也能支持合理高质量的语音。
英文摘要：
Autoregressive speech synthesis often adopts a left-to-right order, yet generation order is a modelling choice. We investigate decoding order through masked diffusion framework, which progressively unmasks positions and allows arbitrary decoding orders during training and inference. By interpolating between identity and random permutations, we show that randomness in decoding order affects speech quality. We further compare fixed strategies, such as \texttt{l2r} and \texttt{r2l} with adaptive ones, such as Top-$K$, finding that fixed-order decoding, including the dominating left-to-right approach, is suboptimal, while adaptive decoding yields better performance. Finally, since masked diffusion requires discrete inputs, we quantise acoustic representations and find that even 1-bit quantisation can support reasonably high-quality speech.
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。
