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This paper proposes a novel technique to obtain better downstream ASRperformance from a joint encoder-decoder self-supervised model when trainedwith speech pooled from two different channels (narrow and wide band). Thejoint encoder-decoder self-supervised model extends the HuBERT model with aTransformer decoder. HuBERT performs clustering of features and predicts theclass of every input frame. In simple pooling, which is our baseline, there isno way to identify the channel information. To incorporate channel information,we have proposed non-overlapping cluster IDs for speech from differentchannels. Our method gives a relative improvement of ~4% over the jointencoder-decoder self-supervised model built with simple pooling of data, whichserves as our baseline.

Channel-Aware Pretraining of Joint Encoder-Decoder Self-Supervised Model for Telephonic-Speech ASR