Medical speech recognition: Reaching parity with humans

Abstract

We present a speech recognition system for the medical domain whose architecture is based on a state-of-the-art stack trained on over 270 h of medical speech data and 30 million tokens of text from clinical episodes. Despite the acoustic challenges and linguistic complexity of the domain, we were able to reduce the system’s word error rate to below 16% in a realistic clinical use case. To further benchmark our system, we determined the human word error rate on a corpus covering a wide variety of speakers, working with multiple medical transcriptionists, and found that our speech recognition system performs on a par with humans.