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