Automatic speech recognition (ASR) based on transducers is widely used. In
training, a transducer maximizes the summed posteriors of all paths. The path
with the highest posterior is commonly defined as the predicted alignment
between the speech and the transcription. While the vanilla transducer does not
have a prior preference for any of the valid paths, this work intends to
enforce the preferred paths and achieve controllable alignment prediction.
Specifically, this work proposes Bayes Risk Transducer (BRT), which uses a
Bayes risk function to set lower risk values to the preferred paths so that the
predicted alignment is more likely to satisfy specific desired properties. We
further demonstrate that these predicted alignments with intentionally designed
properties can provide practical advantages over the vanilla transducer.
Experimentally, the proposed BRT saves inference cost by up to 46% for
non-streaming ASR and reduces overall system latency by 41% for streaming ASR.

基于转录器的自动语音识别（ASR）被广泛使用。本研究提出了 Bayes 风险转录器（BRT），通过使用 Bayes 风险函数将较低风险值设置给优选路径，实现可控的对齐预测，从而提供了逾传统转录器更有实用优势的预测对齐方案，实验结果表明 BRT 在非流式 ASR 中减少了 46% 的推理成本，并在流式 ASR 中降低了 41% 的系统延迟。

贝叶斯风险转导器：具有可控对齐预测的转导器

Bayes Risk Transducer: Transducer with Controllable Alignment Prediction

Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a
target sequence. The Connectionist Temporal Classification (CTC) criterion is
widely used in multiple seq2seq tasks. Besides predicting the target sequence,
a side product of CTC is to predict the alignment, which is the most probable
input-long sequence that specifies a hard aligning relationship between the
input and target units. As there are multiple potential aligning sequences
(called paths) that are equally considered in CTC formulation, the choice of
which path will be most probable and become the predicted alignment is always
uncertain. In addition, it is usually observed that the alignment predicted by
vanilla CTC will drift compared with its reference and rarely provides
practical functionalities. Thus, the motivation of this work is to make the CTC
alignment prediction controllable and thus equip CTC with extra
functionalities. The Bayes risk CTC (BRCTC) criterion is then proposed in this
work, in which a customizable Bayes risk function is adopted to enforce the
desired characteristics of the predicted alignment. With the risk function, the
BRCTC is a general framework to adopt some customizable preference over the
paths in order to concentrate the posterior into a particular subset of the
paths. In applications, we explore one particular preference which yields
models with the down-sampling ability and reduced inference costs. By using
BRCTC with another preference for early emissions, we obtain an improved
performance-latency trade-off for online models. Experimentally, the proposed
BRCTC reduces the inference cost of offline models by up to 47% without
performance degradation and cuts down the overall latency of online systems to
an unseen level.

本文提出 Bayes risk CTC 模型来控制序列预测的对齐，并针对不同需求定制损失函数，实现预测路径优选。实验表明 BRCTC 模型能有效减少离线模型的推断成本并显著降低在线系统的整体延迟。