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In this work, we present a simple but effective method, CTCBERT, foradvancing hidden-unit BERT (HuBERT). HuBERT applies a frame-level cross-entropy(CE) loss, which is similar to most acoustic model training. However, CTCBERTperforms the model training with the Connectionist Temporal Classification(CTC) objective after removing duplicated IDs in each masked region. The ideastems from the observation that there can be significant errors in alignmentswhen using clustered or aligned IDs. CTC learns alignments implicitly,indicating that learning with CTC can be more flexible when misalignmentexists. We examine CTCBERT on IDs from HuBERT Iter1, HuBERT Iter2, and PBERT.The CTC training brings consistent improvements compared to the CE training.Furthermore, when loading blank-related parameters during finetuning, slightimprovements are observed. Evaluated on the Librispeech 960-100h setting, therelative WER improvements of CTCBERT are 2%-11% over HuBERT and PERT ontest-other data.

CTCBERT: Advancing Hidden-unit BERT with CTC Objectives