영어 발성에서 초음파 영상 정보를 이용한 인공신경망 기반의 인강부의 추정과 평가 방법에 대한 연구

Abstract

Electromagnetometers (EMA) have been widely used in articulatory studies as their temporal resolution can capture most speech activities and the fleshpoint information allows one to readily quantify and analyze tongue shape. However, the drawback is that the data lacks details of activity in the pharyngeal region. Several studies have attempted to estimate the unknown pharyngeal shape of the tongue, but few studies are based on unimodal data containing both front and back regions of the tongue at the same time. We use Stone’s ball bearing method to obtain fleshpoint data as well as tongue shape. We further introduce a novel way of connecting balls and attaching them onto the tongue to ensure accurate tracking. An Artificial Neural Network is applied to build a map between observable flesh-points, unknown tongue shape, and pharyngeal region and is optimized to efficiently address nonlinearity.