IMPROVING NONNATIVE SPEECH UNDERSTANDING USING CONTEXT AND N-BEST MEANING FUSION

被引：0

作者：

Xu, Yushi ^{[1
]}

Seneff, Stephanie ^{[1
]}

机构：

[1] MIT, Comp Sci & Artificial Intelligence Lab, Spoken Language Syst Grp, Cambridge, MA 02139 USA

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2012年

关键词：

N-Best Fusion; Spoken Dialogue Systems; Computer-Aided Language Learning;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Speech understanding of nonnative language learners' speech is a challenging problem. In this paper, we investigate the use of dialogue context cues to help improve concept error rate (CER) of nonnative speech in a language learning system. Given that the student's task is known, we show that incorporating the game scores to help select the best hypothesis improves the CER. We also introduce a novel N-best fusion method to create a single final hypothesis on the meaning level. The experimental results show that the fusion methods can further improve the CER.

引用

页码：4977 / 4980

页数：4