Speech Recognition for Medical Conversations

Chung-Cheng Chiu, Anshuman Tripathi, Katherine Chou, Chris Co, Navdeep Jaitly, Diana Jaunzeikare, Anjuli Kannan, Patrick Nguyen, Hasim Sak, Ananth Sankar, Justin Tansuwan, Nathan Wan, Yonghui Wu, Xuedong Zhang

In this paper we document our experiences with developing speech recognition for medical transcription – a system that automatically transcribes doctor-patient conversations. Towards this goal, we built a system along two different methodological lines – a Connectionist Temporal Classification (CTC) phoneme based model and a Listen Attend and Spell (LAS) grapheme based model. To train these models we used a corpus of anonymized conversations representing approximately 14,000 hours of speech. Because of noisy transcripts and alignments in the corpus, a significant amount of effort was invested in data cleaning issues. We describe a two-stage strategy we followed for segmenting the data. The data cleanup and development of a matched language model was essential to the success of the CTC based models. The LAS based models, however were found to be resilient to alignment and transcript noise and did not require the use of language models. CTC models were able to achieve a word error rate of 20.1% and the LAS models were able to achieve 18.3%. Our analysis shows that both models perform well on important medical utterances and therefore can be practical for transcribing medical conversations.

 DOI: 10.21437/Interspeech.2018-40

Cite as: Chiu, C., Tripathi, A., Chou, K., Co, C., Jaitly, N., Jaunzeikare, D., Kannan, A., Nguyen, P., Sak, H., Sankar, A., Tansuwan, J., Wan, N., Wu, Y., Zhang, X. (2018) Speech Recognition for Medical Conversations. Proc. Interspeech 2018, 2972-2976, DOI: 10.21437/Interspeech.2018-40.

  author={Chung-Cheng Chiu and Anshuman Tripathi and Katherine Chou and Chris Co and Navdeep Jaitly and Diana Jaunzeikare and Anjuli Kannan and Patrick Nguyen and Hasim Sak and Ananth Sankar and Justin Tansuwan and Nathan Wan and Yonghui Wu and Xuedong Zhang},
  title={Speech Recognition for Medical Conversations},
  booktitle={Proc. Interspeech 2018},