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    • Bartelds, Martijn (9)
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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 – 9 of 9 1 Automated speech tools for helping communities process restricted-access corpora for language revival efforts ... San, Nay; Bartelds, Martijn; Ògúnrèmí, Tolúlopé. - : arXiv, 2022 BASE Show details 2 Model checkpoints: Wav2Vec 2.0 Large (No finetuning) ... San, Nay; Bartelds, Martijn. - : Zenodo, 2021 BASE Show details 3 Experiment artefacts: DTW search and evaluation (main and pilot experiments) ... San, Nay; Bartelds, Martijn. - : Zenodo, 2021 BASE Show details 4 Model checkpoints: Wav2Vec 2.0 Large (No finetuning) ... San, Nay; Bartelds, Martijn. - : Zenodo, 2021 BASE Show details 5 Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages ... San, Nay; Bartelds, Martijn; Browne, Mitchell; Clifford, Lily; Gibson, Fiona; Mansfield, John; Nash, David; Simpson, Jane; Turpin, Myfany; Vollmer, Maria; Wilmoth, Sasha; Jurafsky, Dan. - : arXiv, 2021 Abstract: Pre-trained speech representations like wav2vec 2.0 are a powerful tool for automatic speech recognition (ASR). Yet many endangered languages lack sufficient data for pre-training such models, or are predominantly oral vernaculars without a standardised writing system, precluding fine-tuning. Query-by-example spoken term detection (QbE-STD) offers an alternative for iteratively indexing untranscribed speech corpora by locating spoken query terms. Using data from 7 Australian Aboriginal languages and a regional variety of Dutch, all of which are endangered or vulnerable, we show that QbE-STD can be improved by leveraging representations developed for ASR (wav2vec 2.0: the English monolingual model and XLSR53 multilingual model). Surprisingly, the English model outperformed the multilingual model on 4 Australian language datasets, raising questions around how to optimally leverage self-supervised speech representations for QbE-STD. Nevertheless, we find that wav2vec 2.0 representations (either English or ... : Accepted at ASRU 2021 ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD URL: https://arxiv.org/abs/2103.14583 https://dx.doi.org/10.48550/arxiv.2103.14583 BASE Hide details 6 Adapting Monolingual Models: Data can be Scarce when Language Similarity is High ... de Vries, Wietse; Bartelds, Martijn; Nissim, Malvina. - : arXiv, 2021 BASE Show details 7 Adapting Monolingual Models: Data can be Scarce when Language Similarity is High ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bartelds, Martijn; de Vries, Wietse. - : Underline Science Inc., 2021 BASE Show details 8 Neural Representations for Modeling Variation in Speech ... Bartelds, Martijn; de Vries, Wietse; Sanal, Faraz. - : arXiv, 2020 BASE Show details 9 A New Acoustic-Based Pronunciation Distance Measure Bartelds, Martijn; Richter, Caitlin; Liberman, Mark... In: Front Artif Intell (2020) BASE Show details

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