In streaming automatic speech recognition (ASR), it is desirable to reduce
latency as much as possible while having minimum impact on recognition
accuracy. Although a few existing methods are able to achieve this goal, they
are difficult to implement due to their dependency on external alignments. In
this paper, we propose a simple way to penalize symbol delay in transducer
model, so that we can balance the trade-off between symbol delay and accuracy
for streaming models without external alignments. Specifically, our method adds
a small constant times (T/2 - t), where T is the number of frames and t is the
current frame, to all the non-blank log-probabilities (after normalization)
that are fed into the two dimensional transducer recursion. For both streaming
Conformer models and unidirectional long short-term memory (LSTM) models,
experimental results show that it can significantly reduce the symbol delay
with an acceptable performance degradation. Our method achieves similar
delay-accuracy trade-off to the previously published FastEmit, but we believe
our method is preferable because it has a better justification: it is
equivalent to penalizing the average symbol delay. Our work is open-sourced and
publicly available (this https URL).

提出一种惩罚转导模型符号延迟的简单方法，来平衡无外部对齐的在线模型特征提取和识别精度之间的权衡，该方法对流式 Conformer 模型和单向 LSTM 模型都可以有效降低符号延迟并且保持可以接受的性能下降。