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Computational modelling of segmental and prosodic levels of analysis for capturing variation across Arabic dialects

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Computational modelling of segmental and prosodic levels of analysis for capturing variation across Arabic dialects. / Brown, Georgina; Hellmuth, Sam.

In: Speech Communication, Vol. 141, 30.06.2022, p. 80-92.

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Brown G, Hellmuth S. Computational modelling of segmental and prosodic levels of analysis for capturing variation across Arabic dialects. Speech Communication. 2022 Jun 30;141:80-92. Epub 2022 Jun 2. doi: 10.1016/j.specom.2022.05.003

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@article{ea3780c71fc148549cbd2aa3e7715320,
title = "Computational modelling of segmental and prosodic levels of analysis for capturing variation across Arabic dialects",
abstract = "Dialect variation spans different linguistic levels of analysis. Two examples include the typical phonetic realisations produced and the typical range of intonational choices made by individuals belonging to a given dialect group. Taking the modelling principles of a specific automatic accent recognition system, the work here characterises and observes the variation that exists within these two levels of analysis among eight Arabic dialects. Using a method that has previously shown promising performance on English accent varieties, we first model the segmental level of analysis from recordings of Arabic speakers to capture the variation in the phonetic realisations of the vowels and consonants. In doing so, we show how powerful this model can be in distinguishing between Arabic dialects. This paper then shows how this modelling approach can be adapted to instead characterise prosodic variation among these same dialects from the same speech recordings. This allows us to inspect the relative power of the segmental and prosodic levels of analysis in separating the Arabic dialects. This work opens up the possibility of using these modelling frameworks to study the extent and nature of phonetic and prosodic variation across speech corpora.",
keywords = "Arabic dialects, AccentIntonation, Automatic accent recognition, Support vector machines",
author = "Georgina Brown and Sam Hellmuth",
year = "2022",
month = jun,
day = "30",
doi = "10.1016/j.specom.2022.05.003",
language = "English",
volume = "141",
pages = "80--92",
journal = "Speech Communication",
issn = "0167-6393",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Computational modelling of segmental and prosodic levels of analysis for capturing variation across Arabic dialects

AU - Brown, Georgina

AU - Hellmuth, Sam

PY - 2022/6/30

Y1 - 2022/6/30

N2 - Dialect variation spans different linguistic levels of analysis. Two examples include the typical phonetic realisations produced and the typical range of intonational choices made by individuals belonging to a given dialect group. Taking the modelling principles of a specific automatic accent recognition system, the work here characterises and observes the variation that exists within these two levels of analysis among eight Arabic dialects. Using a method that has previously shown promising performance on English accent varieties, we first model the segmental level of analysis from recordings of Arabic speakers to capture the variation in the phonetic realisations of the vowels and consonants. In doing so, we show how powerful this model can be in distinguishing between Arabic dialects. This paper then shows how this modelling approach can be adapted to instead characterise prosodic variation among these same dialects from the same speech recordings. This allows us to inspect the relative power of the segmental and prosodic levels of analysis in separating the Arabic dialects. This work opens up the possibility of using these modelling frameworks to study the extent and nature of phonetic and prosodic variation across speech corpora.

AB - Dialect variation spans different linguistic levels of analysis. Two examples include the typical phonetic realisations produced and the typical range of intonational choices made by individuals belonging to a given dialect group. Taking the modelling principles of a specific automatic accent recognition system, the work here characterises and observes the variation that exists within these two levels of analysis among eight Arabic dialects. Using a method that has previously shown promising performance on English accent varieties, we first model the segmental level of analysis from recordings of Arabic speakers to capture the variation in the phonetic realisations of the vowels and consonants. In doing so, we show how powerful this model can be in distinguishing between Arabic dialects. This paper then shows how this modelling approach can be adapted to instead characterise prosodic variation among these same dialects from the same speech recordings. This allows us to inspect the relative power of the segmental and prosodic levels of analysis in separating the Arabic dialects. This work opens up the possibility of using these modelling frameworks to study the extent and nature of phonetic and prosodic variation across speech corpora.

KW - Arabic dialects

KW - AccentIntonation

KW - Automatic accent recognition

KW - Support vector machines

U2 - 10.1016/j.specom.2022.05.003

DO - 10.1016/j.specom.2022.05.003

M3 - Journal article

VL - 141

SP - 80

EP - 92

JO - Speech Communication

JF - Speech Communication

SN - 0167-6393

ER -