Interface Design for Self-Supervised Speech Models
Authors: name: Yi-Jen Shih, name: David Harwath

Self-supervised speech (SSL) models have recently become widely adopted for
many downstream speech processing tasks. The general usage pattern is to employ
SSL models as feature extractors, and then train a downstream prediction head
to solve a specific task. However, different layers of SSL models have been
shown to capture different types of information, and the methods of combining
them are not well studied. To this end, we extend the general framework for SSL
model utilization by proposing the interface that connects the upstream and
downstream. Under this view, the dominant technique of combining features via a
layerwise weighted sum can be regarded as a specific interface. We propose
several alternative interface designs and demonstrate that the weighted sum
interface is suboptimal for many tasks. In particular, we show that a
convolutional interface whose depth scales logarithmically with the depth of
the upstream model consistently outperforms many other interface designs.