We propose autoencoding speaker conversion for training data augmentation in
automatic speech translation. This technique directly transforms an audio
sequence, resulting in audio synthesized to resemble another speaker's voice.
Our method compares favorably to SpecAugment on English$\to$French and
English$\to$Romanian automatic speech translation (AST) tasks as well as on a
low-resource English automatic speech recognition (ASR) task. Further, in
ablations, we show the benefits of both quantity and diversity in augmented
data. Finally, we show that we can combine our approach with augmentation by
machine-translated transcripts to obtain a competitive end-to-end AST model
that outperforms a very strong cascade model on an English$\to$French AST task.
Our method is sufficiently general that it can be applied to other speech
generation and analysis tasks.

本论文提出了使用自动编码说话人转换进行数据增强的方法，通过直接转换音频序列，使其合成声音与另一位说话人类似。我们的方法在英语到法语和英语到罗马尼亚语的自动语音翻译任务上比 SpecAugment 效果更好。同时，我们还展示了数据增强的数量和多样性的好处，并证明了我们可以将该方法与机器翻译的转录结合起来，在英语到法语自动语音翻译任务上胜过一个非常强大的级联模型。该方法具有广泛适用性，可应用于其他语音生成和分析任务。