  This paper proposes a novel technique to obtain better downstream ASR
performance from a joint encoder-decoder self-supervised model when trained
with speech pooled from two different channels (narrow and wide band). The
joint encoder-decoder self-supervised model extends the HuBERT model with a
Transformer decoder. HuBERT performs clustering of features and predicts the
class of every input frame. In simple pooling, which is our baseline, there is
no way to identify the channel information. To incorporate channel information,
we have proposed non-overlapping cluster IDs for speech from different
channels. Our method gives a relative improvement of ~4% over the joint
encoder-decoder self-supervised model built with simple pooling of data, which
serves as our baseline.
