On the Robustness of Arabic Speech Dialect Identification
Peter Sullivan AbdelRahim Elmadany Muhammad Abdul-Mageed
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.