A Study on Building a Voice Cohort for the Development of a Non-face-to-face Machine Learning Diagnostic and Monitoring Platform Using Voice Analysis and Various Sensors in Patients With Dysphagia.

Collection of basic data for real-time analysis and monitoring by measuring what changes in voice occur according to the state of dysphagia using voice analysis and various sensors.

* Design : Prospective cohort study * Setting : Hospital rehabilitation department * Inclusion criteria of the patient group : Patients scheduled for VFSS examination * Intervention * This study will be conducted after collecting basic information such as age, gender, and disease of patients who met the inclusion criteria among patients scheduled to undergo a video fluoroscopic swallowing test (VFSS test) for the diagnosis of dysphagia, and after obtaining guidance and consent through the study description will do. * Before the subject's VFSS test, "Ah for 5 seconds", after clearing the throat, "Ah for 5 seconds", and briefly cut with a slightly high sound, "Ah. oh Ah.", shut lips lightly and read "Ummm\~\~\~\~", read a short sentence. * While the subject makes the above sound, voice is recorded, video is taken, and integrated sensors such as a microphone (sound detection) sensor is attached to measure changes in each sensor. * After the VFSS test, participants will repeat the above sound and record the same twice. (As before the test, imaging of the patient's swallowing is also performed.) * Using voice analysis, various sensor values, and image information, a voice indicator that can predict the amount of suction and residue on the VFSS test is found.