Transformation-based Accented Speech Modeling using Articulatory Attributes for Non-Native Speech Recognition

摘要：

This paper presents a transformation-based approach to robust modeling of accented speech based on articulatory attributes for non-native speech recognition. Firstly, a two-stage verification method is used to extract speech segments from the speech input with non-native accent. Secondly, acoustic models of accented speech are transformed from normal models using linear transformation functions selected from a decision tree to deal with the problem of data sparseness. Thirdly, a discrimination function is applied to filter out the models with low recognition discriminability. Experimental results show that the inclusion of acoustic models of accented speech can eliminate recognition degradation in ASR due to non-native accents and the final ASR system can outperform the standard ASR system in recognizing non-native speech.

作者: Han-Ping Shen Chung-Hsien Wu Pei-Shan Tsai

作者单位: Department of Computer Science and Information Engineering, National Cheng Kung University,Tainan Department of Computer Science and Information Engineering,National Cheng Kung University,Tainan

会议类型: 国际会议

会议名称: 2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)

会议地点: 西安

会议语种:英文

页码: 1-5

在线出版日期: 2011-10-18（万方平台首次上网日期，不代表论文的发表时间）