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

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

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages889-893
Number of pages5
Publication statusPublished - 2011 Dec 1
EventAsia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011 - Xi'an, China
Duration: 2011 Oct 182011 Oct 21

Other

OtherAsia-Pacific Signal and Information Processing Association Annual Summit and Conference 2011, APSIPA ASC 2011
CountryChina
CityXi'an
Period11-10-1811-10-21

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing

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