ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech
  Recognition Using LLMs
Authors: name: Ahmed Heakl, name: Youssef Zaghloul, name: Mennatullah Ali, name: Rania Hossam, name: Walid Gomaa

Motivated by the widespread increase in the phenomenon of code-switching
between Egyptian Arabic and English in recent times, this paper explores the
intricacies of machine translation (MT) and automatic speech recognition (ASR)
systems, focusing on translating code-switched Egyptian Arabic-English to
either English or Egyptian Arabic. Our goal is to present the methodologies
employed in developing these systems, utilizing large language models such as
LLama and Gemma. In the field of ASR, we explore the utilization of the Whisper
model for code-switched Egyptian Arabic recognition, detailing our experimental
procedures including data preprocessing and training techniques. Through the
implementation of a consecutive speech-to-text translation system that
integrates ASR with MT, we aim to overcome challenges posed by limited
resources and the unique characteristics of the Egyptian Arabic dialect.
Evaluation against established metrics showcases promising results, with our
methodologies yielding a significant improvement of 56% in English
translation over the state-of-the-art and 9.3% in Arabic translation. Since
code-switching is deeply inherent in spoken languages, it is crucial that ASR
systems can effectively handle this phenomenon. This capability is crucial for
enabling seamless interaction in various domains, including business
negotiations, cultural exchanges, and academic discourse. Our models and code
are available as open-source resources. Code:
http://github.com/ahmedheakl/arazn-llm}, Models:
http://huggingface.co/collections/ahmedheakl/arazn-llm-662ceaf12777656607b9524e.