A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain

Author:

Qusai Abo Obaidah, Muhy Eddin Zater, Adnan Jaljuli, Ali Mahboub, Asma Hakouz, Bashar Alfrou, Yazan Estaitia

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Computation and Language (cs.CL)

journal:

date:

2024-03-07 00:00:00

Abstract

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.

PDF: A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain.pdf