3 citations · 5 across the 8 of their papers we have counts for
8 papers
NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task
Peter Sullivan, Bashar Talafha, Ahmed Ashraf +11
NADI 2026 is the seventh edition of the Nuanced Arabic Dialect Identification (NADI) shared task series and the second dedicated to multidialectal Arabic speech processing. This ed…
Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology
Peter Sullivan, AbdelRahim Elmadany, Alcides Alcoba Inciarte +1
Dialectal Arabic (DA) speech data vary widely in domain coverage, dialect labeling practices, and recording conditions, complicating cross-dataset comparison and model evaluation.…
Quantum NLP models on Natural Language Inference
Ling Sun, Peter Sullivan, Michael Martin +1
Quantum natural language processing (QNLP) offers a novel approach to semantic modeling by embedding compositional structure directly into quantum circuits. This paper investigates…
NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task
Bashar Talafha, Hawau Olamide Toyin, Peter Sullivan +9
We present the findings of the sixth Nuanced Arabic Dialect Identification (NADI 2025) Shared Task, which focused on Arabic speech dialect processing across three subtasks: spoken…
On Barriers to Archival Audio Processing
Peter Sullivan, Muhammad Abdul-Mageed
In this study, we leverage a unique UNESCO collection of mid-20th century radio recordings to probe the robustness of modern off-the-shelf language identification (LID) and speaker…
On the Robustness of Arabic Speech Dialect Identification
Peter Sullivan, AbdelRahim Elmadany, Muhammad Abdul-Mageed
Arabic dialect identification (ADI) tools are an important part of the large-scale data collection pipelines necessary for training speech recognition models. As these pipelines re…
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…
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning
Toshiko Shibano, Xinyi Zhang, Mia Taige Li +3
To address the performance gap of English ASR models on L2 English speakers, we evaluate fine-tuning of pretrained wav2vec 2.0 models (Baevski et al., 2020; Xu et al., 2021) on L2-…