title: ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech
    Recognition Using LLMs
summary: 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.
paper_authors._id: 667e709e64b6cc4e11f589c5
paper_authors.name: Ahmed Heakl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-28T09:12:58.124Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/656864e12d73834278a8dea7/sfAWS2eyPtFHb_2GZIypp.jpeg
paper_authors.fullname: Ahmed Heakl
paper_authors.type: user
paper_authors.user: ahmedheakl
paper_authors._id: 667e709e64b6cc4e11f589c6
paper_authors.name: Youssef Zaghloul
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-28T13:00:12.828Z
paper_authors._id: 65bd2b5f358734fd09975563
paper_authors.avatarUrl: /avatars/fe0c0f44e923d3c2a23f9b1e64aecadd.svg
paper_authors.fullname: Youssef Zaghloul
paper_authors.type: user
paper_authors.user: zaghloul2012
paper_authors._id: 667e709e64b6cc4e11f589c7
paper_authors.name: Mennatullah Ali
paper_authors._id: 667e709e64b6cc4e11f589c8
paper_authors.name: Rania Hossam
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-28T13:00:30.099Z
paper_authors._id: 6402324afa1acad60064c742
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1677865500544-noauth.jpeg
paper_authors.fullname: Rania hossam elbadry 
paper_authors.type: user
paper_authors.user: Raniahossam33
paper_authors._id: 667e709e64b6cc4e11f589c9
paper_authors.name: Walid Gomaa
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-28T13:00:37.094Z
paper_authors._id: 63b2ba5818e5cf2cdd33d3ad
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1672657431429-noauth.jpeg
paper_authors.fullname: Walid Gomaa
paper_authors.type: user
paper_authors.user: wg-imhotep
paper_title: ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech
    Recognition Using LLMs
paper_summary: 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.
paper_ai_keywords: machine translation (MT) speech recognition (ASR) code-switching LLama Gemma Whisper model speech-to-text translation system data preprocessing training techniques adaptation condition large language models English translation Arabic translation seamless interaction