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

Georgina Brown, Sam Hellmuth

Research output: Contribution to journalArticlepeer-review


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 specific 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.
Original languageEnglish
Pages (from-to)80-92
JournalSpeech Communication
Early online date14 May 2022
Publication statusPublished - 2 Jun 2022

Bibliographical note

© 2022 The Authors.


  • automatic accent recognition
  • Arabic dialects
  • accent
  • intonation
  • Support Vector Machines

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