  With the increased applications of automatic speech recognition (ASR) in
recent years, it is essential to automatically insert punctuation marks and
remove disfluencies in transcripts, to improve the readability of the
transcripts as well as the performance of subsequent applications, such as
machine translation, dialogue systems, and so forth. In this paper, we propose
a Controllable Time-delay Transformer (CT-Transformer) model that jointly
completes the punctuation prediction and disfluency detection tasks in real
time. The CT-Transformer model facilitates freezing partial outputs with
controllable time delay to fulfill the real-time constraints in partial
decoding required by subsequent applications. We further propose a fast
decoding strategy to minimize latency while maintaining competitive
performance. Experimental results on the IWSLT2011 benchmark dataset and an
in-house Chinese annotated dataset demonstrate that the proposed approach
outperforms the previous state-of-the-art models on F-scores and achieves a
competitive inference speed.
