  Self-supervised approaches for speech representation learning are challenged
by three unique problems: (1) there are multiple sound units in each input
utterance, (2) there is no lexicon of input sound units during the pre-training
phase, and (3) sound units have variable lengths with no explicit segmentation.
To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT)
approach for self-supervised speech representation learning, which utilizes an
offline clustering step to provide aligned target labels for a BERT-like
prediction loss. A key ingredient of our approach is applying the prediction
loss over the masked regions only, which forces the model to learn a combined
acoustic and language model over the continuous inputs. HuBERT relies primarily
on the consistency of the unsupervised clustering step rather than the
intrinsic quality of the assigned cluster labels. Starting with a simple
k-means teacher of 100 clusters, and using two iterations of clustering, the
HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0
performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with
10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model,
HuBERT shows up to 19% and 13% relative WER reduction on the more challenging
dev-other and test-other evaluation subsets.
