  Streaming automatic speech recognition (ASR) is very important for many
real-world ASR applications. However, a notable challenge for streaming ASR
systems lies in balancing operational performance against latency constraint.
Recently, a method of chunking, simulating future context and decoding, called
CUSIDE, has been proposed for connectionist temporal classification (CTC) based
streaming ASR, which obtains a good balance between reduced latency and high
recognition accuracy. In this paper, we present CUSIDE-T, which successfully
adapts the CUSIDE method over the recurrent neural network transducer (RNN-T)
ASR architecture, instead of being based on the CTC architecture. We also
incorporate language model rescoring in CUSIDE-T to further enhance accuracy,
while only bringing a small additional latency. Extensive experiments are
conducted over the AISHELL-1, WenetSpeech and SpeechIO datasets, comparing
CUSIDE-T and U2++ (both based on RNN-T). U2++ is an existing counterpart of
chunk based streaming ASR method. It is shown that CUSIDE-T achieves superior
accuracy performance for streaming ASR, with equal settings of latency.
