Abstract
Speech recognition systems play an important role in human-machine interactions. Many systems exist for Arabic speech, however, there are limited systems for dialectal Arabic speech. The Arabic language comprises many properties, some of which are ideal for building automatic speech recognition systems such as syntax and phonology, while other properties are unsuitable for developing speech systems. Importantly, most data are in non-diacritized form, vary in dialect, and contain morphological complexity. Moreover, the Arabic dialects lack a standard structure. In this paper, we highlighted the works and frameworks that have been developed in the last fourteen years of dialectal Arabic speech recognition systems. The paper also presented an analysis and evaluation for several studies using different approaches and techniques. The main goal of this paper is to compare and discuss different studies in dialectal Arabic speech systems including several criteria such as techniques, datasets, evaluation metrics, and dialect types. The study also includes a description of some techniques used in all steps of the dialectal Arabic speech system such as hidden Markov models (HMM), convolutional neural network (CNN), and deep neural network (DNN). In addition, we introduced the challenges and problems of dialectal Arabic speech recognition systems. Overall, more studies are required to obtain a more accurate speech system for dialectal Arabic.