DarSpeech: An Automatic Speech Recognition System for the Moroccan Dialect

Due to the continuous increase of information and data, it has been proven that Automatic Speech Recognition (ASR) systems are more efficient and less expensive when it comes to a variety of important tasks, such as customer relationship management. However, the most complex and accurate speech recognition models are developed and implemented for languages in which data is highly available, such as English and French. This document proposes an automatic speech recognition system for the Moroccan dialect, a very low-resource language, that is spoken by almost every Moroccan citizen and adopted in many organizations that are both public and private. The proposed solution is based on a state-of-the-art architecture, named Deep Speech 2 by Baidu. We tested the model on 24 hours of speech and obtained 22.7% word error rate and 6.03% character error rate.