Disfluency detection is usually an intermediate step between an automatic
speech recognition (ASR) system and a downstream task. By contrast, this paper
aims to investigate the task of end-to-end speech recognition and disfluency
removal. We specifically explore whether it is possible to train an ASR model
to directly map disfluent speech into fluent transcripts, without relying on a
separate disfluency detection model. We show that end-to-end models do learn to
directly generate fluent transcripts; however, their performance is slightly
worse than a baseline pipeline approach consisting of an ASR system and a
disfluency detection model. We also propose two new metrics that can be used
for evaluating integrated ASR and disfluency models. The findings of this paper
can serve as a benchmark for further research on the task of end-to-end speech
recognition and disfluency removal in the future.

探索使用端到端语音识别模型直接将混杂的语音转化为流畅的转录文本的可能性，并且我们提出了两个新的度量标准来评估集成 ASR 和无流畅度模型的性能，该论文的发现可以作为未来关于端到端语音识别和无流畅度处理任务的研究的基准。