Diffusion models have recently been shown to be relevant for high-quality
speech generation. Most work has been focused on generating spectrograms, and
as such, they further require a subsequent model to convert the spectrogram to
a waveform (i.e., a vocoder). This work proposes a diffusion probabilistic
end-to-end model for generating a raw speech waveform. The proposed model is
autoregressive, generating overlapping frames sequentially, where each frame is
conditioned on a portion of the previously generated one. Hence, our model can
effectively synthesize an unlimited speech duration while preserving
high-fidelity synthesis and temporal coherence. We implemented the proposed
model for unconditional and conditional speech generation, where the latter can
be driven by an input sequence of phonemes, amplitudes, and pitch values.
Working on the waveform directly has some empirical advantages. Specifically,
it allows the creation of local acoustic behaviors, like vocal fry, which makes
the overall waveform sounds more natural. Furthermore, the proposed diffusion
model is stochastic and not deterministic; therefore, each inference generates
a slightly different waveform variation, enabling abundance of valid
realizations. Experiments show that the proposed model generates speech with
superior quality compared with other state-of-the-art neural speech generation
systems.

本文提出了一种基于扩散的概率端到端模型，用于生成原始语音波形，该模型通过自回归的方式顺序生成重叠帧，可以实现无限语音时长的合成，并保持高保真度和时间连贯性，通过直接处理波形具有优势，可以创建局部声学行为，同时该模型是随机的，生成略有差异的波形变体，实验结果表明相较于其他最先进的神经语音生成系统，所提出的模型具有更高的合成质量。