Multilingual representations for low resource speech recognition and keyword search

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Self-Supervised Speech Representation Learning: A Review (2022, background): “Multilingual and cross-lingual supervised models have proven superior in low-resource speech recognition tasks [66].”
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition (2021, background): “There has been a great deal of attention to multi-lingual automatic speech recognition (ASR) that can recognize multiple languages [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20].”
Low-Resource Speech Recognition and Keyword-Spotting (2017, methodology): “For OP3 multiple multi-lingual bottleneck features (BN) were made available from Aachen (A28) and IBM (I28), see [4] for details.”
Semi-Supervised and Cross-Lingual Knowledge Transfer Learnings for DNN Hybrid Acoustic Models Under Low-Resource Conditions (2016, methodology): “We notice that an obvious improvement is reported in [5] by combining these two methods with more unsupervised data.”
End-to-End Speech Recognition: A Survey (2023, background): “also help improve ASR development for low resource tasks, again both for classical [298], as well as for E2E systems [299], [300].”
Multilingual Speech Recognition with Corpus Relatedness Sampling (2019, background): “Furthermore, sampling strategies can also be helpful to speed up the training process [21].”