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In this paper, we describe novel components for extracting clinicallyrelevant information from medical conversations which will be available asGoogle APIs. We describe a transformer-based Recurrent Neural NetworkTransducer (RNN-T) model tailored for long-form audio, which can produce richtranscriptions including speaker segmentation, speaker role labeling,punctuation and capitalization. On a representative test set, we compareperformance of RNN-T models with different encoders, units and streamingconstraints. Our transformer-based streaming model performs at about 20% WER onthe ASR task, 6% WDER on the diarization task, 43% SER on periods, 52% SER oncommas, 43% SER on question marks and 30% SER on capitalization. Our recognizeris paired with a confidence model that utilizes both acoustic and lexicalfeatures from the recognizer. The model performs at about 0.37 NCE. Finally, wedescribe a RNN-T based tagging model. The performance of the model depends onthe ontologies, with F-scores of 0.90 for medications, 0.76 for symptoms, 0.75for conditions, 0.76 for diagnosis, and 0.61 for treatments. While there isstill room for improvement, our results suggest that these models aresufficiently accurate for practical applications.

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