  Self-supervised learning (SSL) of speech has shown impressive results in
speech-related tasks, particularly in automatic speech recognition (ASR). While
most methods employ the output of intermediate layers of the SSL model as
real-valued features for downstream tasks, there is potential in exploring
alternative approaches that use discretized token sequences. This approach
offers benefits such as lower storage requirements and the ability to apply
techniques from natural language processing. In this paper, we propose a new
protocol that utilizes discretized token sequences in ASR tasks, which includes
de-duplication and sub-word modeling to enhance the input sequence. It reduces
computational cost by decreasing the length of the sequence. Our experiments on
the LibriSpeech dataset demonstrate that our proposed protocol performs
competitively with conventional ASR systems using continuous input features,
while reducing computational and storage costs.
