# DARTS: Dialectal Arabic Transcription System

arXiv: https://arxiv.org/abs/1909.12163 · HTML: https://mlsanity.com/paper/1909.12163/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Arabic Speech Recognition by End-to-End, Modular Systems and Human](https://mlsanity.com/paper/2101.08454/) (2021)
- [Advancing Arabic Speech Recognition Through Large-Scale Weakly Supervised Learning](https://mlsanity.com/paper/2504.12254/) (2025)

## Abstract (arXiv)

We present the speech to text transcription system, called DARTS, for low resource Egyptian Arabic dialect. We analyze the following; transfer learning from high resource broadcast domain to low-resource dialectal domain and semi-supervised learning where we use in-domain unlabeled audio data collected from YouTube. Key features of our system are: A deep neural network acoustic model that consists of a front end Convolutional Neural Network (CNN) followed by several layers of Time Delayed Neural Network (TDNN) and Long-Short Term Memory Recurrent Neural Network (LSTM); sequence discriminative training of the acoustic model; n-gram and recurrent neural network language model for decoding and N-best list rescoring. We show that a simple transfer learning method can achieve good results. The results are further improved by using unlabeled data from YouTube in a semi-supervised setup. Various systems are combined to give the final system that achieves the lowest word error on on the community standard Egyptian-Arabic speech dataset (MGB-3).