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Improving the Utility of Speech Recognition Through Error Detection

By Kimberly Voll, Stella Atkins and Bruce Forster


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

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Publisher: Springer-Verlag
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Provided by: PubMed Central
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