216 citations · 337 across the 9 of their papers we have counts for
9 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…
Adapting Monolingual Models: Data can be Scarce when Language Similarity is High
Wietse de Vries, Martijn Bartelds, Malvina Nissim +1
For many (minority) languages, the resources needed to train large models are not available. We investigate the performance of zero-shot transfer learning with as little data as po…
As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages
Wietse de Vries, Malvina Nissim
Large generative language models have been very successful for English, but other languages lag behind, in part due to data and computational limitations. We propose a method that…
Neural Representations for Modeling Variation in Speech
Martijn Bartelds, Wietse de Vries, Faraz Sanal +3
Variation in speech is often quantified by comparing phonetic transcriptions of the same utterance. However, manually transcribing speech is time-consuming and error prone. As an a…
What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models
Wietse de Vries, Andreas van Cranenburgh, Malvina Nissim
Peeking into the inner workings of BERT has shown that its layers resemble the classical NLP pipeline, with progressively more complex tasks being concentrated in later layers. To…
BERTje: A Dutch BERT Model
Wietse de Vries, Andreas van Cranenburgh, Arianna Bisazza +3
The transformer-based pre-trained language model BERT has helped to improve state-of-the-art performance on many natural language processing (NLP) tasks. Using the same architectur…