Unconstrained Word Graph Based Keyword Spotting
The performance of keyword spotting system suffers severe degradation when the index stage is so fast that the lattice may lose lots of information to retrieve the spoken terms.In this paper,We focus on this problem and present an approach named unconstraint word graph expansion (UWGE)to keep the pruned hypotheses which are discarded in the decoding procedure but may contain correct hypotheses.The proposed approach is to eliminate the N-gram language model state limitation of lattice and reconstruct lattice to unconstrained word graph.On two Mandarin conversation telephone speech sets,we compare performance using UWGE with that on traditional trigram lattice,and our approach gives satisfying performance gains over trigram lattice.We also show the relationship between the performance and the system speed based on this approach.
spoken term detection unconstraint word graph expansion N-gram lattice limitation
Zhen Zhang Yujing Si Yong Liu Qingwei Zhao Yonghong Yan
The Key Laboratory of Speech Acoustics and Content Understanding Chinese Academy of Sciences,Beijing 100190,P.R.China
国际会议
杭州
英文
1000-1003
2013-03-22（万方平台首次上网日期，不代表论文的发表时间）