  Speech distortions are a long-standing problem that degrades the performance
of supervisely trained speech processing models. It is high time that we
enhance the robustness of speech processing models to obtain good performance
when encountering speech distortions while not hurting the original performance
on clean speech. In this work, we propose to improve the robustness of speech
processing models by domain adversarial training (DAT). We conducted
experiments based on the SUPERB framework on five different speech processing
tasks. In case we do not always have knowledge of the distortion types for
speech data, we analyzed the binary-domain and multi-domain settings, where the
former treats all distorted speech as one domain, and the latter views
different distortions as different domains. In contrast to supervised training
methods, we obtained promising results in target domains where speech data is
distorted with different distortions including new unseen distortions
introduced during testing.
