1 citations · 1 across the 6 of their papers we have counts for
6 papers
Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluation
Reihaneh Amooie, Yun Hao, Wietse de Vries +3
This study explores how bilingual fine-tuning affects automatic speech recognition (ASR) in low-resource languages. We evaluate this method across nine linguistically and geographi…
Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian
Yun Hao, Reihaneh Amooie, Wietse de Vries +2
Automatic speech recognition (ASR) has improved substantially in recent years, yet performance remains limited for low-resource languages. Large language models (LLMs) have shown p…
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance
Reihaneh Amooie, Wietse de Vries, Yun Hao +3
Automatic Speech Recognition (ASR) performance for low-resource languages is still far behind that of higher-resource languages such as English, due to a lack of sufficient labeled…
DUMB: A Benchmark for Smart Evaluation of Dutch Models
Wietse de Vries, Martijn Wieling, Malvina Nissim
We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of datasets for low-, medium- and high-resource tasks. The total set of nine tasks includes four…
Quantifying Language Variation Acoustically with Few Resources
Martijn Bartelds, Martijn Wieling
Deep acoustic models represent linguistic information based on massive amounts of data. Unfortunately, for regional languages and dialects such resources are mostly not available.…
Estimating the Level and Direction of Phonetic Dialect Change in the Northern Netherlands
Raoul Buurke, Hedwig Sekeres, Wilbert Heeringa +2
This article reports ongoing investigations into phonetic change of dialect groups in the northern Netherlandic language area, particularly the Frisian and Low Saxon dialect groups…