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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Survey of business sectors hiring linguists and language professionals ... Gledić, Jelena; Budimirović, Jelena; Đukanović, Maja. - : Zenodo, 2021 BASE Show details 2 Survey of business sectors hiring linguists and language professionals ... Gledić, Jelena; Budimirović, Jelena; Đukanović, Maja. - : Zenodo, 2021 BASE Show details 3 On the Language-specificity of Multilingual BERT and the Impact of Fine-tuning ... Tanti, Marc; van der Plas, Lonneke; Borg, Claudia. - : arXiv, 2021 BASE Show details 4 Data Augmentation for Speech Recognition in Maltese: A Low-Resource Perspective ... Mena, Carlos; DeMarco, Andrea; Borg, Claudia; van der Plas, Lonneke; Gatt, Albert. - : arXiv, 2021 Abstract: Developing speech technologies is a challenge for low-resource languages for which both annotated and raw speech data is sparse. Maltese is one such language. Recent years have seen an increased interest in the computational processing of Maltese, including speech technologies, but resources for the latter remain sparse. In this paper, we consider data augmentation techniques for improving speech recognition for such languages, focusing on Maltese as a test case. We consider three different types of data augmentation: unsupervised training, multilingual training and the use of synthesized speech as training data. The goal is to determine which of these techniques, or combination of them, is the most effective to improve speech recognition for languages where the starting point is a small corpus of approximately 7 hours of transcribed speech. Our results show that combining the three data augmentation techniques studied here lead us to an absolute WER improvement of 15% without the use of a language model. ... : 30 pages; 9 tables ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2111.07793 https://dx.doi.org/10.48550/arxiv.2111.07793 BASE Hide details 5 On the Language-specificity of Multilingual BERT and the Impact of Fine-tuning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Claudia; Gatt, Albert. - : Underline Science Inc., 2021 BASE Show details

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