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Found 5 papers, 2 papers with code
Multilingual acoustic word embeddings for zero-resource languages
This research addresses the challenge of developing speech applications for zero-resource languages that lack labelled data.
Leveraging multilingual transfer for unsupervised semantic acoustic word embeddings
Acoustic word embeddings (AWEs) are fixed-dimensional vector representations of speech segments that encode phonetic content so that different realisations of the same word have similar embeddings.
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili
But in an in-the-wild test on Swahili radio broadcasts with actual hate speech keywords, the AWE model (using one minute of template data) is more robust, giving similar performance to an ASR system trained on 30 hours of labelled data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource language
Through finer-grained analysis, we show that training on even just a single related language gives the largest gain.
Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptation
We consider how a recent contrastive learning loss can be used in both the purely unsupervised and multilingual transfer settings.
