  Masked speech modeling (MSM) methods such as wav2vec2 or w2v-BERT learn
representations over speech frames which are randomly masked within an
utterance. While these methods improve performance of Automatic Speech
Recognition (ASR) systems, they have one major limitation. They treat all
unsupervised speech samples with equal weight, which hinders learning as not
all samples have relevant information to learn meaningful representations. In
this work, we address this limitation. We propose ask2mask (ATM), a novel
approach to focus on specific samples during MSM pre-training. ATM employs an
external ASR model or \textit{scorer} to weight unsupervised input samples in
two different ways: 1) A fine-grained data selection is performed by masking
over the highly confident input frames as chosen by the scorer. This allows the
model to learn meaningful representations. 2) ATM is further extended to focus
at utterance-level by weighting the final MSM loss with the utterance-level
confidence score. We conduct fine-tuning experiments on two well-benchmarked
corpora: LibriSpeech (matching the pre-training data) and Commonvoice,
TED-LIUM, AMI and CHiME-6 (not matching the pre-training data). The results
substantiate the efficacy of ATM on significantly improving the recognition
performance under mismatched conditions (up to 11.6\% relative over published
results and upto 4.46\% relative over our internal baseline) while still
yielding modest improvements under matched conditions.
