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20212025
most citedSpeech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning

3 citations · 10 across the 7 of their papers we have counts for

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cs.CL2025

EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs

Numaan Naeem, Abdellah El Mekki, Muhammad Abdul-Mageed

Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong…

cs.CL20241 cited

Casablanca: Data and Models for Multidialectal Arabic Speech Recognition

Bashar Talafha, Karima Kadaoui, Samar Mohamed Magdy +24

In spite of the recent progress in speech processing, the majority of world languages and dialects remain uncovered. This situation only furthers an already wide technological divi…

cs.CL20221 cited

A Benchmark Study of Contrastive Learning for Arabic Social Meaning

Md Tawkat Islam Khondaker, El Moatez Billah Nagoudi, AbdelRahim Elmadany +2

Contrastive learning (CL) brought significant progress to various NLP tasks. Despite this progress, CL has not been applied to Arabic NLP to date. Nor is it clear how much benefits…

cs.CL20222 cited

AfroLID: A Neural Language Identification Tool for African Languages

Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed +1

Language identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world's 7000+ languages today are not covered by LID tech…

cs.CL20221 cited

Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning

Wei-Rui Chen, Muhammad Abdul-Mageed

Machine translation (MT) involving Indigenous languages, including those possibly endangered, is challenging due to lack of sufficient parallel data. We describe an approach exploi…

cs.CL20222 cited

Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model Decoding

Peter Sullivan, Toshiko Shibano, Muhammad Abdul-Mageed

ASR systems designed for native English (L1) usually underperform on non-native English (L2). To address this performance gap, \textbf{(i)} we extend our previous work to investiga…