  Although frame-based models, such as CTC and transducers, have an affinity
for streaming automatic speech recognition, their decoding uses no future
knowledge, which could lead to incorrect pruning. Conversely, label-based
attention encoder-decoder mitigates this issue using soft attention to the
input, while it tends to overestimate labels biased towards its training
domain, unlike CTC. We exploit these complementary attributes and propose to
integrate the frame- and label-synchronous (F-/L-Sync) decoding alternately
performed within a single beam-search scheme. F-Sync decoding leads the
decoding for block-wise processing, while L-Sync decoding provides the
prioritized hypotheses using look-ahead future frames within a block. We
maintain the hypotheses from both decoding methods to perform effective
pruning. Experiments demonstrate that the proposed search algorithm achieves
lower error rates compared to the other search methods, while being robust
against out-of-domain situations.
