ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech
  Recognition Using LLMs
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.
667e709e64b6cc4e11f589c5
Ahmed Heakl
claimed_verified
2024-06-28T09:12:58.124Z
656864e12d73834278a8dea7
https://cdn-avatars.huggingface.co/v1/production/uploads/656864e12d73834278a8dea7/sfAWS2eyPtFHb_2GZIypp.jpeg
Ahmed Heakl
user
ahmedheakl
667e709e64b6cc4e11f589c6
Youssef Zaghloul
admin_assigned
2024-06-28T13:00:12.828Z
65bd2b5f358734fd09975563
/avatars/fe0c0f44e923d3c2a23f9b1e64aecadd.svg
Youssef Zaghloul
user
zaghloul2012
667e709e64b6cc4e11f589c7
Mennatullah Ali
667e709e64b6cc4e11f589c8
Rania Hossam
admin_assigned
2024-06-28T13:00:30.099Z
6402324afa1acad60064c742
https://cdn-avatars.huggingface.co/v1/production/uploads/1677865500544-noauth.jpeg
Rania hossam elbadry 
user
Raniahossam33
667e709e64b6cc4e11f589c9
Walid Gomaa
admin_assigned
2024-06-28T13:00:37.094Z
63b2ba5818e5cf2cdd33d3ad
https://cdn-avatars.huggingface.co/v1/production/uploads/1672657431429-noauth.jpeg
Walid Gomaa
user
wg-imhotep
ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech
  Recognition Using LLMs
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.
Machine translation and automatic speech recognition systems are developed to handle code-switched Egyptian Arabic-English, utilizing large language models and the Whisper ASR model, showing significant improvements in translation accuracy.
machine translation (MT) automatic speech recognition (ASR) code-switching LLama Gemma Whisper model speech-to-text translation data preprocessing training techniques evaluation metrics