LLM-to-Speech: A Synthetic Data Pipeline for Training Dialectal Text-to-Speech Models (2026 · 1 citation)
Egyptian Arabic is the most widely understood Arabic dialect, yet it has few speech-synthesis resources. Most Arabic TTS work targets MSA or Gulf varieties. The authors build NileTTS, a 38-hour Egyptian Arabic TTS corpus, without recording any human speakers. Their pipeline has four stages:
GATech at AbjadGenEval Shared Task: Multilingual Embeddings for Arabic Machine-Generated Text Classification (2026)
The paper describes a system for the AbjadGenEval shared task, which asks whether an Arabic text was written by a person or produced by an AI model.
GATech at AbjadMed: Bidirectional Encoders vs. Causal Decoders: Insights from 82-Class Arabic Medical Classification (2026)
The paper describes a system for the AbjadMed shared task. The task is to sort Arabic medical questions into 82 categories. The class distribution is heavily skewed, and some gold labels are inconsistent. The author fine-tunes AraBERTv2 with a custom classification head that joins mean-pooled and attention-pooled…

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Updated 1 November 2026