  In the present paper, an attempt is made to combine Mask-CTC and the
triggered attention mechanism to construct a streaming end-to-end automatic
speech recognition (ASR) system that provides high performance with low
latency. The triggered attention mechanism, which performs autoregressive
decoding triggered by the CTC spike, has shown to be effective in streaming
ASR. However, in order to maintain high accuracy of alignment estimation based
on CTC outputs, which is the key to its performance, it is inevitable that
decoding should be performed with some future information input (i.e., with
higher latency). It should be noted that in streaming ASR, it is desirable to
be able to achieve high recognition accuracy while keeping the latency low.
Therefore, the present study aims to achieve highly accurate streaming ASR with
low latency by introducing Mask-CTC, which is capable of learning feature
representations that anticipate future information (i.e., that can consider
long-term contexts), to the encoder pre-training. Experimental comparisons
conducted using WSJ data demonstrate that the proposed method achieves higher
accuracy with lower latency than the conventional triggered attention-based
streaming ASR system.
