The transducer architecture is becoming increasingly popular in the field of
speech recognition, because it is naturally streaming as well as high in
accuracy. One of the drawbacks of transducer is that it is difficult to decode
in a fast and parallel way due to an unconstrained number of symbols that can
be emitted per time step. In this work, we introduce a constrained version of
transducer loss to learn strictly monotonic alignments between the sequences;
we also improve the standard greedy search and beam search algorithms by
limiting the number of symbols that can be emitted per time step in transducer
decoding, making it more efficient to decode in parallel with batches.
Furthermore, we propose an finite state automaton-based (FSA) parallel beam
search algorithm that can run with graphs on GPU efficiently. The experiment
results show that we achieve slight word error rate (WER) improvement as well
as significant speedup in decoding. Our work is open-sourced and publicly
available\footnote{this https URL}.

本文提出了一种带有约束的传递损失，以学习两个序列之间的严格单调对齐，同时优化了标准的贪婪搜索和 beam search 算法，并限制在解码时每个时刻可以传出的符号数，使传感器解码更有效率；作者们还提出了基于有限状态自动机的并行 beam search 算法，可以高效地通过 GPU 运行图形。实验结果表明，我们不仅实现了轻微的错误字率（WER）的改善，而且还实现了显著的解码加速。

转录器快速并行解码

Fast and parallel decoding for transducer

Recurrent neural network models with an attention mechanism have proven to be
extremely effective on a wide variety of sequence-to-sequence problems.
However, the fact that soft attention mechanisms perform a pass over the entire
input sequence when producing each element in the output sequence precludes
their use in online settings and results in a quadratic time complexity. Based
on the insight that the alignment between input and output sequence elements is
monotonic in many problems of interest, we propose an end-to-end differentiable
method for learning monotonic alignments which, at test time, enables computing
attention online and in linear time. We validate our approach on sentence
summarization, machine translation, and online speech recognition problems and
achieve results competitive with existing sequence-to-sequence models.

研究提出一种可用于在线设置中，使得注意力机制在线性时间内计算的可微分方法，- 该方法通过学习单调对齐来解决软注意机制在线性时间复杂度下难以实现的问题，并在句子摘要、机器翻译和在线语音识别领域实现了有竞争力的结果。