  Speech enhancement systems can show improved performance by adapting the
model towards a single test-time speaker. In this personalization context, the
test-time user might only provide a small amount of noise-free speech data,
likely insufficient for traditional fully-supervised learning. One way to
overcome the lack of personal data is to transfer the model parameters from a
speaker-agnostic model to initialize the personalized model, and then to
finetune the model using the small amount of personal speech data. This
baseline marginally adapts over the scarce clean speech data. Alternatively, we
propose self-supervised methods that are designed specifically to learn
personalized and discriminative features from abundant in-the-wild noisy, but
still personal speech recordings. Our experiment shows that the proposed
self-supervised learning methods initialize personalized speech enhancement
models better than the baseline fully-supervised methods, yielding superior
speech enhancement performance. The proposed methods also result in a more
robust feature set under the real-world conditions: compressed model sizes and
fewness of the labeled data.
