On the Robustness of Arabic Speech Dialect Identification
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
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.
Arabic language processing, Arabic speech processing, dialect identification, domain shift, language identification
P. Sullivan, A. Elmadany, and M. Abdul-Mageed, “On the robustness of Arabic speech dialect identification,” INTERSPEECH 2023, Aug 2023. doi:10.21437/interspeech.2023-1005