In modern interactive speech-based systems, speech is consumed and
transcribed incrementally prior to having disfluencies removed. This
post-processing step is crucial for producing clean transcripts and high
performance on downstream tasks (e.g. machine translation). However, most
current state-of-the-art NLP models such as the Transformer operate
non-incrementally, potentially causing unacceptable delays. We propose a
streaming BERT-based sequence tagging model that, combined with a novel
training objective, is capable of detecting disfluencies in real-time while
balancing accuracy and latency. This is accomplished by training the model to
decide whether to immediately output a prediction for the current input or to
wait for further context. Essentially, the model learns to dynamically size its
lookahead window. Our results demonstrate that our model produces comparably
accurate predictions and does so sooner than our baselines, with lower flicker.
Furthermore, the model attains state-of-the-art latency and stability scores
when compared with recent work on incremental disfluency detection.

提出一种基于 BERT 模型的语音识别模型，其能够在实时检测迟滞状态下动态计算其向前看窗口的大小，从而在相较于传统模型有更佳的准确率以及更短的延迟时间，并在增量式迟滞检测研究中实现了最先进的稳定性得分和延迟性得分。