On the Robustness of Arabic Speech Dialect Identification

Peter Sullivan; AbdelRahim Elmadany; Muhammad Abdul-Mageed

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arXiv · 2306.03789

On the Robustness of Arabic Speech Dialect Identification

Peter Sullivan·AbdelRahim Elmadany·Muhammad Abdul-Mageed

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Abstract

Arabic dialect identification (ADI) tools are an important part of the large-scale data collection pipelines necessary for training speech recognition models. As these pipelines require application of ADI tools to potentially out-of-domain data, we aim to investigate how vulnerable the tools may be to this domain shift. With self-supervised learning (SSL) models as a starting point, we evaluate transfer learning and direct classification from SSL features. We undertake our evaluation under rich conditions, with a goal to develop ADI systems from pretrained models and ultimately evaluate performance on newly collected data. In order to understand what factors contribute to model decisions, we carry out a careful human study of a subset of our data. Our analysis confirms that domain shift is a major challenge for ADI models. We also find that while self-training does alleviate this challenges, it may be insufficient for realistic conditions.

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Peter Sullivan, AbdelRahim Elmadany, Muhammad Abdul-Mageed. 2023-06-01. On the Robustness of Arabic Speech Dialect Identification. https://arxiv.org/abs/2306.03789

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