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：非自回归序列到序列语音转换模型因其能有效建模时序结构，同时通过非自回归建模提升语音清晰度和推理速度而备受关注。然而，当前非自回归序列到序列语音转换模型对外部自回归模型提取的真实持续时间存在依赖，这严重限制了其在较小训练数据集上的泛化能力。本文首先通过改变训练数据规模验证上述问题，随后提出AAS-VC——一种基于自动对齐搜索的非自回归序列到序列语音转换模型，该模型消除了对外部持续时间的依赖，并通过提供适当的归纳偏置，为小数据集赋予所需的泛化能力。实验结果表明，AAS-VC能够在仅5分钟的训练数据集上实现更优的泛化效果。此外，我们通过消融研究论证了多项模型设计选择。音频样本和实现代码均已在线公开。