Dr. Ahmed Ali

Ahmed Ali
Principal Software Engineer
Arabic Language Technologies
+(974) 4454 1440

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I am very excited to be a part of QCRI and have the chance to build unique Arabic technology for speech recognition and for education as a whole.

Research Focus at QCRI

Ahmed Ali's research interests are in the area of speech recognition and natural language processing with special attention to dialectal Arabic speech and text processing.

Dr. Ali conducts applied research leading to innovative and practical solutions for impacting people’s daily life.

His current research on speech recognition has led to the development of the state-of-the-art multi-dialectal Arabic speech recognition system, QCRI Advanced Transcription System (QATS). QATS is used by Al Jazeera Arabic to transcribe its online content automatically. The BBC is also using QATS in its media-monitoring platform.

QATS has won first place in the worldwide MGB-2 speech recognition competition. The technology also won the prestigious Qatar Foundation Best Innovation Award in 2018.

Previous Experience

Current: Principal Software Engineer at QCRI, Qatar

2011 – 2016 Senior Software Engineer at QCRI, Qatar

2008 - 2011 Speech Recognition Engineer at Nuance Cambridge, UK

2006 - 2008 Speech Recognition Engineer at SpinVox Maidenhead, UK

1999 - 2006 Software Engineer at IBM, Egypt

Professional Experience


Professional Associations and Awards



Ph.D., Computer Science, The University of Edinburgh, 2014-2018

BS, Electrical Engineering, Cairo University, 1994-1999

Selected Research

 A. Ali, S. Renals, “Word Error Rate Estimation: e-wer", ACL 2018

S. Shon, A. Ali, and J. Glass, “Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition”, Odyssey 2018

A. Ali, S. Vogel, S. Renals, “Speech recognition challenge in the wild: Arabic MGB-3”, ASRU 2017

S. Khurana, M. Najafian, A. Ali, T. Hanai, Y. Belinkov, J. Glass, ”QMDIS: QCRI-MIT Advanced Dialect Identification System”, InterSpeech2017

A. Ali, P. Nakov, P. Bell, S. Renals, “WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition”, ASRU 2017

A. Ali, N. Dehak, P. Cardinal, S. Khurana, S. Yella, J. Glass, P. Bell, S. Renals, "Automatic Dialect Detection in Arabic Broadcast Speech", InterSpeech2016

A. Ali, P. Bell, J. Glass, Y. Messaoui, H. Mubarak, S. Renals, Y. Zhang, "The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition“, SLT 2016

S. Khurana, A. Ali, "QCRI Advanced Transcription System (QATS) for the Arabic Multi-Dialect Broadcast Media Recognition: MGB-2 Challenge", SLT 2016

S. Wray, A. Ali, "Crowdsource a little to label a lot: Labeling a Speech Corpus of Dialectal Arabic."InterSpeech2015

A. Ali, W. Magdy, P. Bell, S. Renals, "Multi-reference WER for evaluating ASR for languages with no orthographic Rules", ASRU 2015

A. Ali, et al. "A complete KALDI recipe for building Arabic speech recognition systems“, SLT 2014

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Upcoming Events

Past Events


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(MLDAS 2019) Machine Learning and Data Analytics Symposium

Download ICS File 01/04/2019  - 02/04/2019 ,

Machine Learning and Data Analytics Symposium - MLDAS 2019 Building on the success of the three previous events , Boeing and QCRI will hold the fifth Machine Learning and Data Analytics Symposium (...

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"Learning to See" Public talk by Professor Antonio Torralba (MIT-CSAIL)

Download ICS File 25/03/2019 ,

Visit by Antonio Torralba, who teaches machines to automate tasks that a human visual system can accomplish, is part of annual spring research update between QCRI and MIT-CSAIL.

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QCRI - MIT CSAIL 2019 Annual Project Review

Download ICS File 25/03/2019 ,

Executive Overview Sessions Open to publi Date: March 25, 2019 Time: 10:15AM - 5:15PM Venue: Hamad Bin Khalia Reseach Complex Multipurpose Room To view agenda, please click here . To RSVP to this ...

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Pact aims to apply data analysis and artificial intelligence techniques to solve humanitarian problems.

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