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On the Benefit of Using Auditory Modeling for Diagnostic Evaluation of Pronunciations
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. (Centre for Speech Technology)
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. (Centre for Speech Technology)ORCID iD: 0000-0001-9277-0288
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology. (Centre for Speech Technology)ORCID iD: 0000-0002-3323-5311
2012 (English)In: International Symposium on Automatic Detection of Errors in Pronunciation Training (IS ADEPT), Stockholm, Sweden, June 6-8, 2012 / [ed] Olov Engwall, 2012, 59-64 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we demonstrate that a psychoacoustic model-based distance measure performs better than a speech signal distance measure in assessing the pronunciation of individual foreign speakers. The experiments show that the perceptual based-method performs not only quantitatively better than a speech spectrum-based method, but also qualitatively better, hence showing that auditory information is beneficial in the task of pronunciation error detection. We first present the general approach of the method, which is using the dissimilarity between the native perceptual domain and the non-native speech power spectrum domain. The problematic phonemes for a given non-native speaker are determined by the degree of disparity between the dissimilarity measure for the non-native and a group of native speakers. The two methods compared here are applied to different groups of non-native speakers of various language backgrounds and validated against a theoretical linguistic study.

Place, publisher, year, edition, pages
2012. 59-64 p.
Keyword [en]
second language learning, auditory model, distortion measure, perceptual assessment, phoneme
National Category
Signal Processing Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-95752OAI: oai:DiVA.org:kth-95752DiVA: diva2:529162
Conference
International Symposium on Automatic Detection of Errors in Pronunciation Training (IS ADEPT), Stockholm, Sweden, June, 2012
Projects
Swedish Research Council project 80449001 Computer-Animated LAnguage TEAchers (CALATEA)
Note

QC 20130110

Available from: 2012-05-29 Created: 2012-05-29 Last updated: 2013-01-10Bibliographically approved

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Engwall, OlovSalvi, Giampiero

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Koniaris, ChristosEngwall, OlovSalvi, Giampiero
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf