We address the problem of formatting the out- put of an automatic speech recognition (ASR) system for readability, while preserving word- level timing information of the transcript. Our system enriches the ASR transcript with punc- tuation, capitalization and properly written dates, times and other numeric entities, and our approach can be applied to other format- ting tasks. The method we describe combines hand-crafted grammars with a class-based lan- guage model trained on written text and relies on Weighted Finite State Transducers (WF- STs) for the preservation of start and end time of each word.

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