6 citations · 18 across the 13 of their papers we have counts for
13 papers
Improving Low-Resource Machine Translation via Round-Trip Reinforcement Learning
Ahmed Attia, Alham Fikri Aji
Low-resource machine translation (MT) has gained increasing attention as parallel data from low-resource language communities is collected, but many approaches for improving low-re…
Articulation-Informed ASR: Integrating Articulatory Features into ASR via Auxiliary Speech Inversion and Cross-Attention Fusion
Ahmed Adel Attia, Jing Liu, Carol Espy Wilson
Prior works have investigated the use of articulatory features as complementary representations for automatic speech recognition (ASR), but their use was largely confined to shallo…
RealClass: A Framework for Classroom Speech Simulation with Public Datasets and Game Engines
Ahmed Adel Attia, Jing Liu, Carol Espy Wilson
The scarcity of large-scale classroom speech data has hindered the development of AI-driven speech models for education. Classroom datasets remain limited and not publicly availabl…
SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research
Ahmed Adel Attia, Jing Liu, Carl Espy-Wilson
The scarcity of large-scale classroom speech data has hindered the development of AI-driven speech models for education. Public classroom datasets remain limited, and the lack of a…
Multi-Stage Speaker Diarization for Noisy Classrooms
Ali Sartaz Khan, Tolulope Ogunremi, Ahmed Adel Attia +1
Speaker diarization, the process of identifying "who spoke when" in audio recordings, is essential for understanding classroom dynamics. However, classroom settings present distinc…
From Weak Labels to Strong Results: Utilizing 5,000 Hours of Noisy Classroom Transcripts with Minimal Accurate Data
Ahmed Adel Attia, Dorottya Demszky, Jing Liu +1
Recent progress in speech recognition has relied on models trained on vast amounts of labeled data. However, classroom Automatic Speech Recognition (ASR) faces the real-world chall…
CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments
Ahmed Adel Attia, Dorottya Demszky, Tolulope Ogunremi +2
Creating Automatic Speech Recognition (ASR) systems that are robust and resilient to classroom conditions is paramount to the development of AI tools to aid teachers and students.…
Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings
Ahmed Adel Attia, Dorottya Demszky, Tolulope Ogunremi +2
Creating Automatic Speech Recognition (ASR) systems that are robust and resilient to classroom conditions is paramount to the development of AI tools to aid teachers and students.…
Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables
Ahmed Adel Attia, Yashish M. Siriwardena, Carol Espy-Wilson
The performance of deep learning models depends significantly on their capacity to encode input features efficiently and decode them into meaningful outputs. Better input and outpu…
Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults
Ahmed Adel Attia, Jing Liu, Wei Ai +2
Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance give…
Enhancing Speech Articulation Analysis using a Geometric Transformation of the X-ray Microbeam Dataset
Ahmed Adel Attia, Mark Tiede, Carol Y. Espy-Wilson
Accurate analysis of speech articulation is crucial for speech analysis. However, X-Y coordinates of articulators strongly depend on the anatomy of the speakers and the variability…
Masked Autoencoders Are Articulatory Learners
Ahmed Adel Attia, Carol Espy-Wilson
Articulatory recordings track the positions and motion of different articulators along the vocal tract and are widely used to study speech production and to develop speech technolo…
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
Yashish M. Siriwardena, Ahmed Adel Attia, Ganesh Sivaraman +1
Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developin…