Transformation-based accented speech modeling using articulatory attributes for non-native speech recognition

Han Ping Shen, Chung-Hsien Wu, Pei Shan Tsai

研究成果: Paper同行評審

2 引文 斯高帕斯(Scopus)

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.

原文English
頁面889-893
頁數5
出版狀態Published - 2011 十二月 1
事件Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011 - Xi'an, China
持續時間: 2011 十月 182011 十月 21

Other

OtherAsia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011
國家China
城市Xi'an
期間11-10-1811-10-21

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing

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