activity
20172022
most citedDARTS: Dialectal Arabic Transcription System

5 citations · 7 across the 5 of their papers we have counts for

collaborators

10 papers

cs.CL20221 cited

Creating Speech-to-Speech Corpus from Dubbed Series

Massa Baali, Wassim El-Hajj, Ahmed Ali

Dubbed series are gaining a lot of popularity in recent years with strong support from major media service providers. Such popularity is fueled by studies that showed that dubbed v…

cs.CL2021

Arabic Code-Switching Speech Recognition using Monolingual Data

Ahmed Ali, Shammur Chowdhury, Amir Hussein +1

Code-switching in automatic speech recognition (ASR) is an important challenge due to globalization. Recent research in multilingual ASR shows potential improvement over monolingua…

cs.CL20211 cited

QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus

Hamdy Mubarak, Amir Hussein, Shammur Absar Chowdhury +1

We introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. This multi-dialect speech dataset contains 2,000 hours of speech sampled at 16…

eess.AS2021

Arabic Speech Recognition by End-to-End, Modular Systems and Human

Amir Hussein, Shinji Watanabe, Ahmed Ali

Recent advances in automatic speech recognition (ASR) have achieved accuracy levels comparable to human transcribers, which led researchers to debate if the machine has reached hum…

eess.AS2020

Word Error Rate Estimation Without ASR Output: e-WER2

Ahmed Ali, Steve Renals

Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-consum…

cs.CL20195 cited

DARTS: Dialectal Arabic Transcription System

Sameer Khurana, Ahmed Ali, James Glass

We present the speech to text transcription system, called DARTS, for low resource Egyptian Arabic dialect. We analyze the following; transfer learning from high resource broadcast…