It is estimated that around 70 million people worldwide are affected by a
speech disorder called stuttering. With recent advances in Automatic Speech
Recognition (ASR), voice assistants are increasingly useful in our everyday
lives. Many technologies in education, retail, telecommunication and healthcare
can now be operated through voice. Unfortunately, these benefits are not
accessible for People Who Stutter (PWS). We propose a simple but effective
method called 'Detect and Pass' to make modern ASR systems accessible for
People Who Stutter in a limited data setting. The algorithm uses a context
aware classifier trained on a limited amount of data, to detect acoustic frames
that contain stutter. To improve robustness on stuttered speech, this extra
information is passed on to the ASR model to be utilized during inference. Our
experiments show a reduction of 12.18% to 71.24% in Word Error Rate (WER)
across various state of the art ASR systems. Upon varying the threshold of the
associated posterior probability of stutter for each stacked frame used in
determining low frame rate (LFR) acoustic features, we were able to determine
an optimal setting that reduced the WER by 23.93% to 71.67% across different
ASR systems.

为解决语音助手对口吃 (Speech Disorder) 人士不友好的问题，研究提出了一种简单高效的称为 “Detect and Pass” 的算法，它通过一个上下文相关的对口吃音频帧分类器，对口吃的音频进行筛选，再将这些筛选的结果传递到 ASR 模型中提高其错误率的鲁棒性。该研究表明在不同的 ASR 系统上，WER 的降低幅度达到了 23.93% 到 71.67%.