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Leveraging Pre-Trained Representations to Improve Access to Untranscribed Speech from Endangered Languages.

, , , , , , , , , , , and . ASRU, page 1094-1101. IEEE, (2021)

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A New Acoustic-Based Pronunciation Distance Measure., , , and . Frontiers Artif. Intell., (2020)Neural representations for modeling variation in speech., , , , , and . J. Phonetics, (2022)Automated speech tools for helping communities process restricted-access corpora for language revival efforts., , , , , , , , and . CoRR, (2022)Leveraging neural representations for facilitating access to untranscribed speech from endangered languages., , , , , , , , , and 2 other author(s). CoRR, (2021)Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation., , , , and . ACL (1), page 715-729. Association for Computational Linguistics, (2023)Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions., , , , , , , , , and . CoRR, (2023)Neural Representations for Modeling Variation in English Speech., , , , , and . CoRR, (2020)Quantifying Language Variation Acoustically with Few Resources., and . NAACL-HLT, page 3735-3741. Association for Computational Linguistics, (2022)Adapting Monolingual Models: Data can be Scarce when Language Similarity is High., , , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 4901-4907. Association for Computational Linguistics, (2021)Leveraging Pre-Trained Representations to Improve Access to Untranscribed Speech from Endangered Languages., , , , , , , , , and 2 other author(s). ASRU, page 1094-1101. IEEE, (2021)