  The bootstrap resampling method has been popular for performing significance
analysis on word error rate (WER) in automatic speech recognition (ASR)
evaluation. To deal with dependent speech data, the blockwise bootstrap
approach is also introduced. By dividing utterances into uncorrelated blocks,
this approach resamples these blocks instead of original data. However, it is
typically nontrivial to uncover the dependent structure among utterances and
identify the blocks, which might lead to subjective conclusions in statistical
testing. In this paper, we present graphical lasso based methods to explicitly
model such dependency and estimate uncorrelated blocks of utterances in a
rigorous way, after which blockwise bootstrap is applied on top of the inferred
blocks. We show the resulting variance estimator of WER in ASR evaluation is
statistically consistent under mild conditions. We also demonstrate the
validity of proposed approach on LibriSpeech dataset.
