  In this paper, we present TrimTail, a simple but effective emission
regularization method to improve the latency of streaming ASR models. The core
idea of TrimTail is to apply length penalty (i.e., by trimming trailing frames,
see Fig. 1-(b)) directly on the spectrogram of input utterances, which does not
require any alignment. We demonstrate that TrimTail is computationally cheap
and can be applied online and optimized with any training loss or any model
architecture on any dataset without any extra effort by applying it on various
end-to-end streaming ASR networks either trained with CTC loss [1] or
Transducer loss [2]. We achieve 100 $\sim$ 200ms latency reduction with equal
or even better accuracy on both Aishell-1 and Librispeech. Moreover, by using
TrimTail, we can achieve a 400ms algorithmic improvement of User Sensitive
Delay (USD) with an accuracy loss of less than 0.2.
