  This work presents self-supervised learning methods for developing monaural
speaker-specific (i.e., personalized) speech enhancement models. While
generalist models must broadly address many speakers, specialist models can
adapt their enhancement function towards a particular speaker's voice,
expecting to solve a narrower problem. Hence, specialists are capable of
achieving more optimal performance in addition to reducing computational
complexity. However, naive personalization methods can require clean speech
from the target user, which is inconvenient to acquire, e.g., due to subpar
recording conditions. To this end, we pose personalization as either a
zero-shot task, in which no additional clean speech of the target speaker is
used for training, or a few-shot learning task, in which the goal is to
minimize the duration of the clean speech used for transfer learning. With this
paper, we propose self-supervised learning methods as a solution to both zero-
and few-shot personalization tasks. The proposed methods are designed to learn
the personalized speech features from unlabeled data (i.e., in-the-wild noisy
recordings from the target user) without knowing the corresponding clean
sources. Our experiments investigate three different self-supervised learning
mechanisms. The results show that self-supervised models achieve zero-shot and
few-shot personalization using fewer model parameters and less clean data from
the target user, achieving the data efficiency and model compression goals.
