  Existing studies on self-supervised speech representation learning have
focused on developing new training methods and applying pre-trained models for
different applications. However, the quality of these models is often measured
by the performance of different downstream tasks. How well the representations
access the information of interest is less studied. In this work, we take a
closer look into existing self-supervised methods of speech from an
information-theoretic perspective. We aim to develop metrics using mutual
information to help practical problems such as model design and selection. We
use linear probes to estimate the mutual information between the target
information and learned representations, showing another insight into the
accessibility to the target information from speech representations. Further,
we explore the potential of evaluating representations in a self-supervised
fashion, where we estimate the mutual information between different parts of
the data without using any labels. Finally, we show that both supervised and
unsupervised measures echo the performance of the models on layer-wise linear
probing and speech recognition.
