A system for real time collaborative transcription correction

Peter Bell, Joachim Fainberg, Catherine Lai, Mark Sinclair

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a system to enable efficient, collaborative human correction of ASR transcripts, designed to operate in real-time situations, for example, when post-editing live captions generated for news broadcasts. In the system, confusion networks derived from ASR lattices are used to highlight low-confident words and present alternatives to the user for quick correction. The system uses a client-server architecture, whereby information about each manual edit is posted to the server. Such information can be used to dynamically update the one-best ASR output for all utterances currently in the editing pipeline. We propose to make updates in three different ways; by finding a new one-best path through an existing ASR lattice consistent with the correction received; by identifying further instances of out-of-vocabulary terms entered by the user; and by adapting the language model on the fly. Updates are received asynchronously by the client.
Original languageEnglish
Title of host publicationInterspeech 2017
PublisherInternational Speech Communication Association
Number of pages2
Publication statusPublished - 24 Aug 2017
EventInterspeech 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017

Publication series

PublisherInternational Speech Communication Association
ISSN (Print)1990-9772


ConferenceInterspeech 2017
Internet address


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