Automatic Editing in a Back-End Speech-to-Text System

Proceedings of ACL-08: HLT, pages 114–120, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Automatic Editing in a Back-End Speech-to-Text System Maximilian Bisani Paul Vozila Olivier Divay Jeff Adams Nuance Communications One Wayside Road Burlington, MA 01803, U.S.A. {maximilian.bisani,paul.vozila,olivier.divay,jeff.adams}@nuance.com Abstract Written documents created through dictation differ significantly from a true verbatim tran- script of the recorded speech. This poses an obstacle in automatic dictation systems as speech recognition output needs to undergo a fair amount of editing in order to turn it into a document that complies with the cus- tomary standards. We present an approach that attempts to perform this edit from recog- nized words to final document automatically by learning the appropriate transformations from example documents. This addresses a number of problems in an integrated way, which have so far been studied independently, in particular automatic punctuation, text seg- mentation, error correction and disfluency re- pair. We study two different learning methods, one based on rule induction and one based on a probabilistic sequence model. Quantitative evaluation shows that the probabilistic method performs more accurately. 1 Introduction Large vocabulary speech recognition today achieves a level of accuracy that makes it useful in the produc- tion of written documents. Especially in the medical and legal domains large volumes of text are tradi- tionally produced by means of dictation. Here docu- ment creation is typically a “back-end” process. The author dictates all necessary information into a tele- phone handset or a portable recording device and is not concerned with the actual production of the document any further. A transcriptionist will then listen to the recorded dictation and produce a well- formed document using a word processor. The goal of introducing speech recognition in this process is to create a draft document automatically, so that the transcriptionist only has to verify the accuracy of the document and to fix occasional recognition errors. We observe that users try to spend as little time as possible dictating. They usually focus only on the content and rely on the transcriptionist to compose a readable, syntactically correct, stylistically accept- able and formally compliant document. For this rea- son there is a considerable discrepancy between the final document and what the speaker has said liter- ally. In particular in medical reports we see differ- ences of the following kinds: • Punctuation marks are typically not verbalized. • No instructions on the formatting of the report are dictated. Section headings are not identified as such. • Frequently section headings are only implied. (“vitals are” → “PHYSICAL EXAMINATION: VITAL SIGNS:”) • Enumerated lists. Typically speakers use phrases like “number one ...next number ...”, which need to be turned into “1. ...2. ...” • The dictation usually begins with a preamble (e.g. “This is doctor Xyz ...”) which does not appear in the report. Similarly there are typ- ical phrases at the end of the dictation which should not be transcribed (e.g. “End of dicta- tion. Thank you.”) 114 • There are specific standards regarding the use …

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