Non-autoregressive (non-AR) sequence-to-seqeunce (seq2seq) models for voice conversion (VC) is attractive in its ability to effectively model the temporal structure while enjoying boosted intelligibility and fast inference thanks to non-AR modeling. However, the dependency of current non-AR seq2seq VC models on ground truth durations extracted from an external AR model greatly limits its generalization ability to smaller training datasets. In this paper, we first demonstrate the above-mentioned problem by varying the training data size. Then, we present AAS-VC, a non-AR seq2seq VC model based on automatic alignment search (AAS), which removes the dependency on external durations and serves as a proper inductive bias to provide the required generalization ability for small datasets. Experimental results show that AAS-VC can generalize better to a training dataset of only 5 minutes. We also conducted ablation studies to justify several model design choices. The audio samples and implementation are available online.

翻译：非自回归（非AR）序列到序列（seq2seq）语音转换（VC）模型因其能够有效建模时序结构，同时通过非AR建模实现增强的可懂度和快速推理而具有吸引力。然而，当前非AR seq2seq VC模型对外部AR模型提取的真实持续时间的依赖，严重限制了其在较小训练数据集上的泛化能力。在本文中，我们首先通过改变训练数据量来证明上述问题。然后，我们提出AAS-VC，一种基于自动对齐搜索（AAS）的非AR seq2seq VC模型，该模型消除了对外部持续时间的依赖，并作为适当的归纳偏置，为小数据集提供了所需的泛化能力。实验结果表明，AAS-VC能够更好地泛化到仅5分钟的训练数据集。我们还进行了消融研究，以验证若干模型设计选择。音频样本和实现代码可在网上获取。