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.

翻译：摘要：扩散模型近期已被证实适用于高质量语音生成。现有研究多集中于频谱图生成，仍需借助后续模型（即声码器）将频谱图转换为波形。本文提出了一种用于生成原始语音波形的扩散概率端到端模型。该模型采用自回归架构，通过顺序生成重叠帧实现，其中每一帧都基于先前生成帧的部分信息进行条件生成。因此，本模型可在保持高保真合成与时间连贯性的同时，有效合成任意长度的语音。我们实现了无条件与条件语音生成两种模式，其中条件生成可通过输入的音素、振幅与音高序列进行驱动。直接处理波形具有若干经验性优势：具体而言，其可生成如气泡音等局部声学特征，使整体波形更趋自然。此外，所提扩散模型具有随机性而非确定性，因此每次推理均会生成略异的波形变体，从而产生丰富的有效实现。实验表明，与当前最先进的神经语音生成系统相比，本模型生成的语音具有更优质量。