Building a Korean conversational speech database in the emergency medical domain

Abstract

This paper describes a method of building Korean conversational speech data in the emergency medical domain and proposes an annotation method for the collected data in order to improve speech recognition performance. To suggest future research directions, baseline speech recognition experiments were conducted by using partial data that were collected and annotated. All voices were recorded at 16-bit resolution at 16 kHz sampling rate. A total of 166 conversations were collected, amounting to 8 hours and 35 minutes. Various information was manually transcribed such as orthography, pronunciation, dialect, noise, and medical information using Praat. Baseline speech recognition experiments were used to depict problems related to speech recognition in the emergency medical domain. The Korean conversational speech data presented in this paper are first-stage data in the emergency medical domain and are expected to be used as training data for developing conversational systems for emergency medical applications.

Accuracy of Cloud-Based Speech Recognition Open Application Programming Interface for Medical Terms of Korean

“…SR technology is becoming widely used in healthcare services in areas ranging from simple remote symptom checks for individuals self-quarantining due to coronavirus disease 2019 to the documentation of medical records and classification of emergent patients. 11 12 Most of the time spent in EHR is allotted to the documentation of health records, and there were definite benefits in documentation speed seen when using SR technology, and this time-saving affects other benefits, work productivity, and cost-effectiveness. 1 13 14 Despite this, evidence about the benefits of SR usage for clinical documentation is implausible and relatively neutral due to its own barriers, such as the requirement of training to use SR and interoperability with existing systems.…”