融合查询扩展和动态匹配的集外词检测

目前关键词检测面临的一个主要挑战是集外词问题。由于集外词发音的不确定性导致其检测性能与集内词相差很多。对此，本文提出了一种融合查询扩展和动态匹配的方法来改善集外词检测的性能。首先比较了基于联合多元模型的查询扩展和基于最小编辑距离的动态匹配。考虑到二者潜在的互补性，采用两种融合方法：一种方法是结果融合，分别应用查询扩展和动态匹配并行的检测集外词，然后合并检测结果；另一种是置信度融合，融合最小编辑距离和发音得分构成混合置信度进行集外词的检出与确认。实验结果表明，第二种融合方法的效果更好，系统的品质因数相对提升了19.8%。

Incorporating Query Expansion into Dynamic Match for Out-Of-Vocabulary Word Detection

Nowadays, one of the challenges of keyword spotting is the issue of out-of-vocabulary (OOV) word. The detection performance for OOV word is considerably worse than for in-vocabulary (INV) word due to its high degree of uncertainty in pronunciation. This paper presents a method to improve the OOV word detection performance by incorporating query expansion into dynamic match. We initially compare the joint-multigram model based query expansion and the minimum edit distance (MED) based dynamic match for OOV word. Considering the potential mutual complementarity between them, we propose two methods of fusion. One is result fusion: performing a parallel OOV word detection with query expansion and dynamic match individually and then merging search results of the two systems. Another is confidence fusion: combining MED and the pronunciation score together as a hybrid confidence measure to implement OOV word detection and verification. Tests show that the second fusion method is more efficient and the figure of merit achieves 19.8% improvement relatively.