Articulatory Comparison of L1 and L2 Speech for Mispronunciation Diagnosis

Subash Khanal; Michael T. Johnson; Narjes Bozorg

doi:10.1109/SLT48900.2021.9383574

Articulatory Comparison of L1 and L2 Speech for Mispronunciation Diagnosis

Subash Khanal, Michael T. Johnson, Narjes Bozorg

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

This paper compares the difference in articulation patterns between native (L1) and non-native (L2) Mandarin speakers of English, for the purpose of providing an understanding of mispronunciation behaviors of L2 learners. Consensus transcriptions from the Electromagnetic Articulography Mandarin Accented English (EMA-MAE) corpus are used to identify commonly occurring substitution errors for consonants and vowels. Phoneme level alignments of the utterances produced by speech recognition models are used to extract articulatory feature vectors representing correct and substituted sounds from L1 and L2 speaker groups respectively. The articulatory features that are significantly different between the two groups are identified along with the direction of error for the L2 speaker group. Experimental results provide information about which types of substitutions are most common and which specific articulators are the most significant contributors to those errors.

Original language	English
Title of host publication	2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
Pages	693-697
Number of pages	5
ISBN (Electronic)	9781728170664
DOIs	https://doi.org/10.1109/SLT48900.2021.9383574
State	Published - Jan 19 2021
Event	2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Virtual, Shenzhen, China Duration: Jan 19 2021 → Jan 22 2021

Publication series

Name	2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings

Conference

Conference	2021 IEEE Spoken Language Technology Workshop, SLT 2021
Country/Territory	China
City	Virtual, Shenzhen
Period	1/19/21 → 1/22/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

Articulatory features
Mispronunciation Diagnosis

ASJC Scopus subject areas

Linguistics and Language
Language and Linguistics
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Hardware and Architecture

Access to Document

10.1109/SLT48900.2021.9383574

Cite this

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title = "Articulatory Comparison of L1 and L2 Speech for Mispronunciation Diagnosis",

abstract = "This paper compares the difference in articulation patterns between native (L1) and non-native (L2) Mandarin speakers of English, for the purpose of providing an understanding of mispronunciation behaviors of L2 learners. Consensus transcriptions from the Electromagnetic Articulography Mandarin Accented English (EMA-MAE) corpus are used to identify commonly occurring substitution errors for consonants and vowels. Phoneme level alignments of the utterances produced by speech recognition models are used to extract articulatory feature vectors representing correct and substituted sounds from L1 and L2 speaker groups respectively. The articulatory features that are significantly different between the two groups are identified along with the direction of error for the L2 speaker group. Experimental results provide information about which types of substitutions are most common and which specific articulators are the most significant contributors to those errors.",

keywords = "Articulatory features, Mispronunciation Diagnosis",

author = "Subash Khanal and Johnson, {Michael T.} and Narjes Bozorg",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Spoken Language Technology Workshop, SLT 2021 ; Conference date: 19-01-2021 Through 22-01-2021",

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doi = "10.1109/SLT48900.2021.9383574",

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Khanal, S, Johnson, MT & Bozorg, N 2021, Articulatory Comparison of L1 and L2 Speech for Mispronunciation Diagnosis. in 2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings., 9383574, 2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings, pp. 693-697, 2021 IEEE Spoken Language Technology Workshop, SLT 2021, Virtual, Shenzhen, China, 1/19/21. https://doi.org/10.1109/SLT48900.2021.9383574

Articulatory Comparison of L1 and L2 Speech for Mispronunciation Diagnosis. / Khanal, Subash; Johnson, Michael T.; Bozorg, Narjes.
2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings. 2021. p. 693-697 9383574 (2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Articulatory Comparison of L1 and L2 Speech for Mispronunciation Diagnosis

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AU - Johnson, Michael T.

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N2 - This paper compares the difference in articulation patterns between native (L1) and non-native (L2) Mandarin speakers of English, for the purpose of providing an understanding of mispronunciation behaviors of L2 learners. Consensus transcriptions from the Electromagnetic Articulography Mandarin Accented English (EMA-MAE) corpus are used to identify commonly occurring substitution errors for consonants and vowels. Phoneme level alignments of the utterances produced by speech recognition models are used to extract articulatory feature vectors representing correct and substituted sounds from L1 and L2 speaker groups respectively. The articulatory features that are significantly different between the two groups are identified along with the direction of error for the L2 speaker group. Experimental results provide information about which types of substitutions are most common and which specific articulators are the most significant contributors to those errors.

AB - This paper compares the difference in articulation patterns between native (L1) and non-native (L2) Mandarin speakers of English, for the purpose of providing an understanding of mispronunciation behaviors of L2 learners. Consensus transcriptions from the Electromagnetic Articulography Mandarin Accented English (EMA-MAE) corpus are used to identify commonly occurring substitution errors for consonants and vowels. Phoneme level alignments of the utterances produced by speech recognition models are used to extract articulatory feature vectors representing correct and substituted sounds from L1 and L2 speaker groups respectively. The articulatory features that are significantly different between the two groups are identified along with the direction of error for the L2 speaker group. Experimental results provide information about which types of substitutions are most common and which specific articulators are the most significant contributors to those errors.

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Articulatory Comparison of L1 and L2 Speech for Mispronunciation Diagnosis

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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