Spoken Term Detection Based on Acoustic Models Trained in Multiple Languages for Zero-Resource Language

摘要

In this paper, we study a spoken term detection method for zero-resource languages by using rich-resource languages. The examined method combines phonemic posteriorgrams (PPGs) extracted from phonemic classifiers of multiple languages and detects a query word based on dynamic time warping. As a result, the method showed better detection performance in a zero-resource language compared with the method using PPGs of a single language.