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

Brown, Georgina and Hellmuth, Sam (2022) Computational modelling of segmental and prosodic levels of analysis for capturing variation across Arabic dialects. Speech Communication, 141. pp. 80-92. ISSN 0167-6393

[img]
Text (Brown_and_Hellmuth_ManuscriptSC_revision_v4)
Brown_and_Hellmuth_ManuscriptSC_revision_v4.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (759kB)

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.

Item Type:
Journal Article
Journal or Publication Title:
Speech Communication
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
170476
Deposited By:
Deposited On:
17 May 2022 10:25
Refereed?:
Yes
Published?:
Published
Last Modified:
14 Jan 2023 02:20