Speech recognition for medical conversations
Abstract
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations ($14,000$ hr), designed the task to represent the real word scenario, and explored several alignment approaches to iteratively improve data quality. We explored both CTC and LAS systems for building speech recognition models. The LAS was more resilient to noisy data and CTC required more data clean up. A detailed analysis is provided for understanding the performance for clinical tasks. Our analysis showed the speech recognition models performed well on important medical utterances, while errors occurred in causal conversations. Overall we believe the resulting models can provide reasonable quality in practice.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2017
- DOI:
- 10.48550/arXiv.1711.07274
- arXiv:
- arXiv:1711.07274
- Bibcode:
- 2017arXiv171107274C
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Sound;
- Electrical Engineering and Systems Science - Audio and Speech Processing;
- Statistics - Machine Learning
- E-Print:
- Interspeech 2018 camera ready