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UBC Theses and Dissertations

Representation learning for Arabic dialect identification Sullivan, Peter


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).

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