  Hidden-unit BERT (HuBERT) is a widely-used self-supervised learning (SSL)
model in speech processing. However, we argue that its fixed 20ms resolution
for hidden representations would not be optimal for various speech-processing
tasks since their attributes (e.g., speaker characteristics and semantics) are
based on different time scales. To address this limitation, we propose
utilizing HuBERT representations at multiple resolutions for downstream tasks.
We explore two approaches, namely the parallel and hierarchical approaches, for
integrating HuBERT features with different resolutions. Through experiments, we
demonstrate that HuBERT with multiple resolutions outperforms the original
model. This highlights the potential of utilizing multiple resolutions in SSL
models like HuBERT to capture diverse information from speech signals.
