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With the increased applications of automatic speech recognition (ASR) inrecent years, it is essential to automatically insert punctuation marks andremove disfluencies in transcripts, to improve the readability of thetranscripts as well as the performance of subsequent applications, such asmachine translation, dialogue systems, and so forth. In this paper, we proposea Controllable Time-delay Transformer (CT-Transformer) model that jointlycompletes the punctuation prediction and disfluency detection tasks in realtime. The CT-Transformer model facilitates freezing partial outputs withcontrollable time delay to fulfill the real-time constraints in partialdecoding required by subsequent applications. We further propose a fastdecoding strategy to minimize latency while maintaining competitiveperformance. Experimental results on the IWSLT2011 benchmark dataset and anin-house Chinese annotated dataset demonstrate that the proposed approachoutperforms the previous state-of-the-art models on F-scores and achieves acompetitive inference speed.

Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection