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.
|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 18 → 2011 十月 21
|Other||Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011|
|期間||11-10-18 → 11-10-21|
All Science Journal Classification (ASJC) codes