BILINEAR CONFIDENCE WARPING BASED ON COALESCENCE TYPE IN KOREAN KEYWORD SPOTTING
Xu Ji, Yeming Xiao, Jielin Pan, Yonghong Yan · 2014
Korean is an agglutinative language in which word boundaries are not explicit. Thus, subword units are usually used in large-vocabulary speech recognition (LVCSR) or LVCSR based application like keyword spotting. Coalescence degree is new word property defined by this paper, which describes the quantity and length of sub-word units decomposed from a word. Bilinear confidence warping modifies confidence measure for keyword candidate base on its coalescence degree. Experiments show that performance can be significantly improved when bilinear confidence warping is used.