As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
In this work we document the development of an ASR system for the transcription of conversations between patient and doctor and we will point out the critical aspects of the domain. The system was trained with an acoustic base of spontaneous speech that has a domain language model and a supervised phonetic dictionary. Its performance was compared with two systems: a) NeMo End-to-End Conformers in Spanish and b) Google API ASR (Automatic Speech Recognition) Cloud. The evaluation was carried out on a set of 208 teleconsultations recorded during the year 2020. The WER (Word Error Rate) was evaluated in ASR, and Recall and F1 for recognized medical entities. In conclusion, the developed system performed better, reaching 72.5% accuracy in the domain of teleconsultations and an F1 for entity recognition of 0.80.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.