This thesis has two aims: developing resources for Arabic dialects and improving the speech recognition of Arabic dialects. Two important components are considered: Pronunciation Dictionary (PD) and Language Model (LM). Six parts are involved, which relate to finding and evaluating dialects resources and improving the performance of systems for the speech recognition of dialects. Three resource...

Arabs throughout the Arab world speak different dialects of Arabic in their daily conversations. We envision collecting a data set that maps different Arabic variations and dialects to Modern Standard Arabic (MSA). These mappings can be then used to facilitate the communication among Arabs from different regions. In this work, we developed a Game With A Purpose (GWAP) to collect mappings betwee...

In this paper, we investigate different approaches in crowdsourcing transcriptions of Dialectal Arabic speech with automatic quality control to ensure good transcription at the source. Since Dialectal Arabic has no standard orthographic representation, it is very challenging to perform quality control. We propose a complete recipe for speech transcription quality control that includes using out...

In this paper we describe a system developed to identify a set of four regional Arabic dialects (Egyptian, Gulf, Levantine, North African) and Modern Standard Arabic (MSA) in a transcribed speech corpus. We competed under the team name MAZA in the Arabic Dialect Identification sub-task of the 2016 Discriminating between Similar Languages (DSL) shared task. Our system achieved an F1-score of 0.5...

This paper introduces a supervised approach for performing sentence level dialect identification between Modern Standard Arabic and Egyptian Dialectal Arabic. We use token level labels to derive sentence-level features. These features are then used with other core and meta features to train a generative classifier that predicts the correct label for each sentence in the given input text. The sy...

Arabic dialects present a special problem for natural language processing because there are few Arabic dialect resources, they have no standard orthography, and they have not been studied much. However, as more and more written dialectal Arabic is found on social media, natural language processing for Arabic dialects has become an important goal. We present a methodology for creating a morpholo...

To date, majority of research for Arabic Named Entity Recognition (NER) addresses the task for Modern Standard Arabic (MSA) and mainly focuses on the newswire genre. Despite some common characteristics between MSA and Dialectal Arabic (DA), the significant differences between the two language varieties hinder such MSA specific systems from solving NER for Dialectal Arabic. In this paper, we pre...

We present a generalized discriminative model for spelling error correction which targets character-level transformations. While operating at the character level, the model makes use of wordlevel and contextual information. In contrast to previous work, the proposed approach learns to correct a variety of error types without guidance of manuallyselected constraints or language-specific features...