Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction

Understand

In this paper, we describe novel components for extracting clinically relevant information from medical conversations which will be available as Google APIs.

• We describe a transformer-based Recurrent Neural Network Transducer (RNN-T) model tailored for long-form audio, which can produce rich transcriptions including speaker segmentation, speaker role labeling, punctuation and capitalization.
• On a representative test set, we compare performance of RNN-T models with different encoders, units and streaming constraints.
• Our transformer-based streaming model performs at about 20% WER on the ASR task, 6% WDER on the diarization task, 43% SER on periods, 52% SER on commas, 43% SER on question marks and 30% SER on capitalization.