14 citations · 39 across the 15 of their papers we have counts for
21 papers
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking
Fahim Dalvi, Maram Hasanain, Sabri Boughorbel +10
The recent development and success of Large Language Models (LLMs) necessitate an evaluation of their performance across diverse NLP tasks in different languages. Although several…
FOOCTTS: Generating Arabic Speech with Acoustic Environment for Football Commentator
Massa Baali, Ahmed Ali
This paper presents FOOCTTS, an automatic pipeline for a football commentator that generates speech with background crowd noise. The application gets the text from the user, applie…
QVoice: Arabic Speech Pronunciation Learning Application
Yassine El Kheir, Fouad Khnaisser, Shammur Absar Chowdhury +3
This paper introduces a novel Arabic pronunciation learning application QVoice, powered with end-to-end mispronunciation detection and feedback generator module. The application is…
LAraBench: Benchmarking Arabic AI with Large Language Models
Ahmed Abdelali, Hamdy Mubarak, Shammur Absar Chowdhury +13
Recent advancements in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. Despite this progress, these models lack specific b…
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition
Injy Hamed, Amir Hussein, Oumnia Chellah +5
Code-switching poses a number of challenges and opportunities for multilingual automatic speech recognition. In this paper, we focus on the question of robust and fair evaluation m…
SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation
Yassine El Kheir, Shammur Absar Chowdhury, Ahmed Ali +2
The lack of labeled second language (L2) speech data is a major challenge in designing mispronunciation detection models. We introduce SpeechBlender - a fine-grained data augmentat…
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…
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition
Amir Hussein, Shammur Absar Chowdhury, Ahmed Abdelali +3
The pervasiveness of intra-utterance code-switching (CS) in spoken content requires that speech recognition (ASR) systems handle mixed language. Designing a CS-ASR system has many…
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…
What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis
Shammur Absar Chowdhury, Nadir Durrani, Ahmed Ali
Deep neural networks are inherently opaque and challenging to interpret. Unlike hand-crafted feature-based models, we struggle to comprehend the concepts learned and how they inter…
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…
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…
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…
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…
Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition
Yonatan Belinkov, Ahmed Ali, James Glass
End-to-end neural network systems for automatic speech recognition (ASR) are trained from acoustic features to text transcriptions. In contrast to modular ASR systems, which contai…
WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition
Ahmed Ali, Preslav Nakov, Peter Bell +1
We study the problem of evaluating automatic speech recognition (ASR) systems that target dialectal speech input. A major challenge in this case is that the orthography of dialects…
Speech Recognition Challenge in the Wild: Arabic MGB-3
Ahmed Ali, Stephan Vogel, Steve Renals
This paper describes the Arabic MGB-3 Challenge - Arabic Speech Recognition in the Wild. Unlike last year's Arabic MGB-2 Challenge, for which the recognition task was based on more…
MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge
Suwon Shon, Ahmed Ali, James Glass
In order to successfully annotate the Arabic speech con- tent found in open-domain media broadcasts, it is essential to be able to process a diverse set of Arabic dialects. For the…
Multi-view Dimensionality Reduction for Dialect Identification of Arabic Broadcast Speech
Sameer Khurana, Ahmed Ali, Steve Renals
In this work, we present a new Vector Space Model (VSM) of speech utterances for the task of spoken dialect identification. Generally, DID systems are built using two sets of featu…
The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition
Ahmed Ali, Peter Bell, James Glass +4
This paper describes the Arabic Multi-Genre Broadcast (MGB-2) Challenge for SLT-2016. Unlike last year's English MGB Challenge, which focused on recognition of diverse TV genres, t…
Automatic Dialect Detection in Arabic Broadcast Speech
Ahmed Ali, Najim Dehak, Patrick Cardinal +5
We investigate different approaches for dialect identification in Arabic broadcast speech, using phonetic, lexical features obtained from a speech recognition system, and acoustic…