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Journal of Imaging Informatics in Medicine

Published in:

01-12-2008

Improving the Utility of Speech Recognition Through Error Detection

Authors: Kimberly Voll, Ph. D., Stella Atkins, Ph. D., Bruce Forster, M. D.,

Published in: Journal of Imaging Informatics in Medicine | Issue 4/2008

Abstract

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.

Horii SC, Redfern R, Kundel H, et al: PACS technologies and reliability: are we making things better or worse? Proc SPIE 4685:16–24, 2002CrossRef

Mehta A, Dreyer K, Schweitzer A, et al: Voice recognition—an emerging necessity within radiology: Experiences of the Massachusetts general hospital. J Digit Imaging 11:20–23, 1998PubMedCrossRef

Al-Aynati NM, Chorneyko KA: Comparison of voice-automated transcription and human transcription in generating pathology reports. Arch Pathol Lab Med 127(6):721–725, 2003PubMed

Ranaa DS, Hurst G, Shepstone L, et al: Voice recognition for radiology reports: is it good enough? Clin Radiol 60(11):1205–1212, 2005CrossRef

Marion J: Radiologists’ attitudes can make or break speech recognition. Diagn Imaging Online http://www.superiorconsultant.com/Pressroom/Articles/Diagnostic%20Imaging%20Feb%202002.doc

Gale B, Safriel Y, Lukban A: Radiology report production times: voice recognition vs. transcription. Radiol Manage 23:18–22, 2001PubMed

Jeong M, Kim B, Lee G: Using higher-level linguistic knowledge for speech recognition error correction in a spoken Q/A dialog. In: Proceedings of the HLT-NAACL special workshop on Higher-Level Linguistic Information for Speech Processing, Boston, USA, 2004, pp 48–55

Jurafsky D, Martin J: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Englewood Cliffs: Prentice-Hall, 2000

Allen JF, Miller BW, Ringger EK, et al: A robust system for natural spoken dialogue. In: Proceedings of the 34th Annual Meeting of the ACL, Santa Cruz, USA, 1996, pp 62–70

10.

Kaki S, Sumita E, Iida H: A method for correcting errors in speech recognition using the statistical features of character co-occurrence. In: ACL-COLING, Montreal, Canada, 1998, pp 653–657

11.

Sarma A, Palmer D: Context-based speech recognition error detection and correction. In: Proceedings of the HLT-NAACL, Boston, USA, 2004, pp 85–88

12.

Inkpen D, Désilets A: Semantic similarity for detecting recognition errors in automatic speech transcripts. In: Proceedings of EMNLP. Association for Computational Linguistics, Vancouver, Canada, 2005, pp 49–56, http://www.aclweb.org/anthology/H/H05/H05-1007

13.

Manning CD, Schütze H: Foundations of Statistical Natural Language Processing. Cambridge: MIT Press, 2002

14.

Voll K: A Methodology of Error Detection: Improving Speech Recognition in Radiology. Ph.D. thesis, Simon Fraser University, 2006

15.

Sistrom C: Conceptual approach for the design of radiology reporting interfaces: the talking template. J Digit Imaging 18(3):176–187, 2005PubMedCrossRef

16.

Caviedes JE, Cimino JJ: Towards the development of a conceptual distance metric for the UMLS. J Biomed Inform 37:77–85, 2004PubMedCrossRef

17.

Shiffman S, Detmer WMS, Lane, CD, et al: A continuous-speech interface to a decision support system: I. Techniques to accommodate misrecognized input. J Am Med Inform Assoc 2:36–45, 1995PubMed

Title: Improving the Utility of Speech Recognition Through Error Detection
Authors: Kimberly Voll, Ph. D.
Stella Atkins, Ph. D.
Bruce Forster, M. D.,
Publication date: 01-12-2008
Publisher: Springer-Verlag
Published in: Journal of Imaging Informatics in Medicine / Issue 4/2008
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-007-9034-7

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Improving the Utility of Speech Recognition Through Error Detection

Abstract

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