Representation learning for Arabic dialect identification
department or school: Arts, Faculty of
department or school: iSchool (Library, Archival and Information Studies)
data provider: DSpace
degree campus: UBCV
creator: Sullivan, Peter
issued: 2022-08-22T23:09:15Z
issued: 2022
related degree: Master of Library and Information Studies - MLIS
degree grantor: University of British Columbia
description: Arabic dialect identification (ADI) is an important aspect of the Arabic speech processing pipeline, and in particular dialectal Arabic automatic speech recognition (ASR) models. In this work, we present an overview of corpora and methods applicable to both ADI and dialectal Arabic ASR, then we benchmark two approaches to using pre-trained speech representation models for ADI. Namely, we first employ direct fine-tuning, and then use fixed-representations extracted from pre-trained models as an intermediate step in the ADI process. We train and evaluate our models on the granular ADI-17 Arabic dialect corpus (92% F1 for our fine-tuned HuBERT model), and further probe generalization by evaluating our trained models on coarse-grained ADI-5, (80% F1 for fine-tuned HuBERT).
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/82448
note: Representation Learning for ArabicDialect IdentificationbyPeter SullivanB.Sc., (Physics), Massachusetts Institute of Technology, 2008A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF LIBRARY AND INFORMATION STUDIESinThe Faculty of Graduate and Postdoctoral Studies(Library and Information Studies)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)August 2022© Peter Sullivan 2022The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the thesis entitled:Representation Learning for Arabic Dialect Identificationsubmitted by Peter Sullivan in partial fulfilment of the requirements for the degree ofMaster of Library and Information Studies:Examining Committee:Muhammad Abdul-Mageed, Associate Professor, School of Information, UBCSupervisorLuanne Sinnamon, Associate Professor, School of Information, UBCSupervisory Committee MemberMiikka Silfverberg, Assistant Professor, Department of Linguistics, UBCSupervisory Committee MemberiiAbstractArabic dialect identification (ADI) is an important aspect of the Arabic speech processingpipeline, and in particular dialectal Arabic automatic speech recognition (ASR) models.In this work, we present an overview of corpora and methods applicable to both ADIand dialectal Arabic ASR, then we benchmark two approaches to using pre-trained speechrepresentation models for ADI. Namely, we first employ direct fine-tuning, and then usefixed-representations extracted from pre-trained models as an intermediate step in theADI process. We train and evaluate our models on the granular ADI-17 Arabic dialectcorpus (92% F1 for our fine-tuned HuBERT model), and further probe generalization byevaluating our trained models on coarse-grained ADI-5, (80% F1 for fine-tuned HuBERT).iiiLay SummaryAutomatic dialect identification allows for categorization of spoken audio clips into appro-priate dialect or language. We apply a strategy called transfer lear…
has type: Thesis/Dissertation
date issued: 2022-11
is shown at: 10.14288/1.0417468
language: eng
degree discipline: Library and Information Studies
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Abdul-Mageed, Muhammad
type: Text
identifier URI: http://hdl.handle.net/2429/82448