Towards Understanding ASR Error Correction for Medical Conversations
Anirudh Mani, Shruti Palaskar, Sandeep Konam · 2020
Domain Adaptation for Automatic SpeechRecognition (ASR) error correction via machine translation is a useful technique for improving out-of-domain outputs of pre-trained ASR systems to obtain optimal results for specific in-domain tasks.We use this technique on our dataset of Doctor-Patient conversations using two off-the-shelf ASR systems: Google ASR (commercial) and the ASPIRE model (open-source).We train a Sequenceto-Sequence Machine Translation model and evaluate it on seven specific UMLS Semantic types, including Pharmacological Substance, Sign or Symptom, and Diagnostic Procedure to name a few.Lastly, we breakdown, analyze and discuss the 7% overall improvement in word error rate in view of each Semantic type.