Unsupervised representation learning for speech audios attained impressive
performances for speech recognition tasks, particularly when annotated speech
is limited. However, the unsupervised paradigm needs to be carefully designed
and little is known about what properties these representations acquire. There
is no guarantee that the model learns meaningful representations for valuable
information for recognition. Moreover, the adaptation ability of the learned
representations to other domains still needs to be estimated. In this work, we
explore learning domain-invariant representations via a direct mapping of
speech representations to their corresponding high-level linguistic
informations. Results prove that the learned latents not only capture the
articulatory feature of each phoneme but also enhance the adaptation ability,
outperforming the baseline largely on accented benchmarks.

本文探讨通过将语音表征映射到对应的高级语言信息以学习领域不变的语音表征，结果证明，学习到的 latents 不仅捕捉到每个音素的发音特征，而且提高了适应能力，在 accened 测试基准上大幅优于基准模型。