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In this paper, we present TrimTail, a simple but effective emissionregularization method to improve the latency of streaming ASR models. The coreidea 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 notrequire any alignment. We demonstrate that TrimTail is computationally cheapand can be applied online and optimized with any training loss or any modelarchitecture on any dataset without any extra effort by applying it on variousend-to-end streaming ASR networks either trained with CTC loss [1] orTransducer loss [2]. We achieve 100 $\sim$ 200ms latency reduction with equalor even better accuracy on both Aishell-1 and Librispeech. Moreover, by usingTrimTail, we can achieve a 400ms algorithmic improvement of User SensitiveDelay (USD) with an accuracy loss of less than 0.2.

TrimTail: Low-Latency Streaming ASR with Simple but Effective Spectrogram-Level Length Penalty