A New Benchmark for Evaluating Automatic Speech Recognition in the
  Arabic Call Domain
Qusai Abo Obaidah Muhy Eddin Za'ter Adnan Jaljuli Ali Mahboub Asma Hakouz Bashar Alfrou Yazan Estaitia
This work is an attempt to introduce a comprehensive benchmark for Arabic
speech recognition, specifically tailored to address the challenges of
telephone conversations in Arabic language. Arabic, characterized by its rich
dialectal diversity and phonetic complexity, presents a number of unique
challenges for automatic speech recognition (ASR) systems. These challenges are
further amplified in the domain of telephone calls, where audio quality,
background noise, and conversational speech styles negatively affect
recognition accuracy. Our work aims to establish a robust benchmark that not
only encompasses the broad spectrum of Arabic dialects but also emulates the
real-world conditions of call-based communications. By incorporating diverse
dialectical expressions and accounting for the variable quality of call
recordings, this benchmark seeks to provide a rigorous testing ground for the
development and evaluation of ASR systems capable of navigating the
complexities of Arabic speech in telephonic contexts. This work also attempts
to establish a baseline performance evaluation using state-of-the-art ASR
technologies.