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

Producción científica: Conference contribution › revisión exhaustiva

7 Citas (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
Páginas	693-697
Número de páginas	5
ISBN (versión digital)	9781728170664
DOI	https://doi.org/10.1109/SLT48900.2021.9383574
Estado	Published - ene 19 2021
Evento	2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Virtual, Online, China Duración: ene 19 2021 → ene 22 2021

Serie de la publicación

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

Conference

Conference	2021 IEEE Spoken Language Technology Workshop, SLT 2021
País/Territorio	China
Ciudad	Virtual, Online
Período	1/19/21 → 1/22/21

Nota bibliográfica

Publisher Copyright:
© 2021 IEEE.

ASJC Scopus subject areas

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

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10.1109/SLT48900.2021.9383574

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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",

year = "2021",

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day = "19",

doi = "10.1109/SLT48900.2021.9383574",

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booktitle = "2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings",

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Khanal, S, Johnson, MT & Bozorg, N 2021, Articulatory Comparison of L1 and L2 Speech for Mispronunciation Diagnosis. En 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, Online, 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).

Producción científica: Conference contribution › revisión exhaustiva

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

AU - Khanal, Subash

AU - Johnson, Michael T.

AU - Bozorg, Narjes

PY - 2021/1/19

Y1 - 2021/1/19

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

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