  In this work, we propose novel decoding algorithms to enable streaming
automatic speech recognition (ASR) on unsegmented long-form recordings without
voice activity detection (VAD), based on monotonic chunkwise attention (MoChA)
with an auxiliary connectionist temporal classification (CTC) objective. We
propose a block-synchronous beam search decoding to take advantage of efficient
batched output-synchronous and low-latency input-synchronous searches. We also
propose a VAD-free inference algorithm that leverages CTC probabilities to
determine a suitable timing to reset the model states to tackle the
vulnerability to long-form data. Experimental evaluations demonstrate that the
block-synchronous decoding achieves comparable accuracy to the
label-synchronous one. Moreover, the VAD-free inference can recognize long-form
speech robustly for up to a few hours.
