Crowdsourced Continuous Improvement of Medical Speech Recognition
Salloum, Wael (EMR.AI) | Edwards, Erik (EMR.AI) | Ghaffarzadegan, Shabnam (EMR.AI) | Suendermann-Oeft, David (EMR.AI) | Miller, Mark (EMR.AI)
Doctor John Doe dictating") are removed, directives to transcribers Dictated medical reports pose many challenges to automatic (e.g., "thank you for writing this", "I'm sorry, remove speech recognition (ASR) due to the linguistic complexity this") are skipped, and explanatory phrases (e.g., "first of the domain, the acoustic environment, and the way name Kevin K E V for Victor I N") and repeated words (e.g., speakers handle dictation. First, highly complex, domainspecific "she uh she") are handled. Moreover, auto-formatting corrects medical terminology including thousands of drug partial phrases in context (e.g., "temp" to "temperature"); names render the use of standard language models ineffective.
Feb-4-2017
- Country:
- North America > United States > California > San Francisco County > San Francisco (0.14)
- Industry:
- Technology:
- Information Technology > Artificial Intelligence
- Speech > Speech Recognition (1.00)
- Natural Language (1.00)
- Machine Learning (0.95)
- Information Technology > Artificial Intelligence