Improving the utility of speech recognition through error detection.
Despite the potential to dominate radiology reporting, current speech recognition technology is thus far a weak and inconsistent alternative to traditional human transcription. This is attributable to poor accuracy rates, in spite of vendor claims, and the wasted resources that go into correcting er...
| Publicado en: | Journal of Digital Imaging Vol. 21; no. 4; pp. 371 - 378 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105569276&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105569276 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2008 vid: 21 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105569276 2010092430 10.1007/s10278-007-9034-7 NLM17387554 105569276 ppf: 371 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Improving the utility of speech recognition through error detection. aug: au: Voll K Atkins S Forster B affil: School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, V5A1S6 Canada sug: subj: Medical Transcription Radiography Voice Recognition Systems Automation Evaluation Research Funding Source Information Retrieval Reports Human ab: Despite the potential to dominate radiology reporting, current speech recognition technology is thus far a weak and inconsistent alternative to traditional human transcription. This is attributable to poor accuracy rates, in spite of vendor claims, and the wasted resources that go into correcting erroneous reports. A solution to this problem is post-speech-recognition error detection that will assist the radiologist in proofreading more efficiently. In this paper, we present a statistical method for error detection that can be applied after transcription. The results are encouraging, showing an error detection rate as high as 96% in some cases. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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