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 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.
Voll
Kimberly
K
School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A1S6, Canada. kvoll@cs.sfu.ca
Atkins
Stella
S
Forster
Bruce
B
D003627:Data Interpretation, Statistical; D006801:Humans; D016347:Medical Records Systems, Computerized / Q000592:standards*; D011871:Radiology; D011873:Radiology Information Systems / Q000592:standards*; D015203:Reproducibility of Results; D012984:Software; D049250:Speech Recognition Software / Q000592:standards*