Self-supervised representations have been extensively studied for
discriminative and generative tasks. However, their robustness capabilities
have not been extensively investigated. This work focuses on self-supervised
representations for spoken generative language models. First, we empirically
demonstrate how current state-of-the-art speech representation models lack
robustness to basic signal variations that do not alter the spoken information.
To overcome this, we propose an effective and efficient method to learn robust
self-supervised speech representation for generative spoken language modeling.
The proposed approach is based on applying a set of signal transformations to
the speech signal and optimizing the model using an iterative pseudo-labeling
scheme. Our method significantly improves over the evaluated baselines when
considering encoding metrics. We additionally evaluate our method on the
speech-to-speech translation task. We consider Spanish-English and
French-English conversions and empirically demonstrate the benefits of
following the proposed approach.

本文探讨了基于自监督学习的语音表征模型的鲁棒性问题，提出了一种基于伪标注技术的有效学习自监督语音表征的方法，该方法可显著提高语音编码的效果，并在语音翻译任务中得到了验证。