  This paper proposes a novel non-autoregressive (NAR) block-based Attention
Mask Decoder (AMD) that flexibly balances performance-efficiency trade-offs for
Conformer ASR systems. AMD performs parallel NAR inference within contiguous
blocks of output labels that are concealed using attention masks, while
conducting left-to-right AR prediction and history context amalgamation between
blocks. A beam search algorithm is designed to leverage a dynamic fusion of
CTC, AR Decoder, and AMD probabilities. Experiments on the LibriSpeech-100hr
corpus suggest the tripartite Decoder incorporating the AMD module produces a
maximum decoding speed-up ratio of 1.73x over the baseline CTC+AR decoding,
while incurring no statistically significant word error rate (WER) increase on
the test sets. When operating with the same decoding real time factors,
statistically significant WER reductions of up to 0.7% and 0.3% absolute (5.3%
and 6.1% relative) were obtained over the CTC+AR baseline.
