Human speech data comprises a rich set of domain factors such as accent,
syntactic and semantic variety, or acoustic environment. Previous work explores
the effect of domain mismatch in automatic speech recognition between
pre-training and fine-tuning as a whole but does not dissect the contribution
of individual factors. In this paper, we present a controlled study to better
understand the effect of such factors on the performance of pre-trained
representations. To do so, we pre-train models either on modified natural
speech or synthesized audio, with a single domain factor modified, and then
measure performance on automatic speech recognition after fine tuning. Results
show that phonetic domain factors play an important role during pre-training
while grammatical and syntactic factors are far less important. To our
knowledge, this is the first study to better understand the domain
characteristics in self-supervised pre-training for speech.

本文通过对自监督预训练语音中不同领域因素的实验研究，发现语音中的语音学因素在预训练时具有重要作用，而语法和句法因素较不重要，这是首次探究预训练语音中的领域特征。