Cross-Lingual Speech Model Performance Gains from Expanded Flemish Dutch Pre-training Data

Abstract

Abstract: Multilingual BERT (mBERT) However, these evaluations have focused on cross-lingual transfer with highresource languages, covering only a third of the languages covered by mBERT. We explore how mBERT performs on a much wider set of languages, focusing on the quality of representation for low-resource languages, measured by within-language performance. We consider three tasks: Named Entity Recognition (99 languages), Part-of-speech Tagging, and Dependency Parsing (54 languages each). mBERT does better than or comparable to baselines on high resource languages but does much worse for low resource

Research Question

To what extent does increasing the amount of Flemish Dutch pre-training data improve the cross-lingual transfer performance of speech models, as evaluated by WER on the LibriSpeech and VoxForge corpora for both high- and low-resource languages?

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