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Making more of little data: Improving low-resource automatic speech recognition using data augmentation
M Bartelds, N San, B McDonnell, D Jurafsky… - ar** communities process restricted-access corpora for language revival efforts
Many archival recordings of speech from endangered languages remain unannotated and
inaccessible to community members and language learning programs. One bottleneck is the …
inaccessible to community members and language learning programs. One bottleneck is the …
From'snippet-lects' to doculects and dialects: Leveraging neural representations of speech for placing audio signals in a language landscape
XLSR-53, a multilingual model of speech, builds a vector representation from audio, which
allows for a range of computational treatments. The experiments reported here use this …
allows for a range of computational treatments. The experiments reported here use this …
Unsupervised discovery of recurring spoken terms using diagonal patterns
Spoken term discovery is a challenging task when a lot of spoken content is generated
without annotation. The spoken term discovery task accomplished by pattern matching …
without annotation. The spoken term discovery task accomplished by pattern matching …
On the nature of discrete speech representations in multilingual self-supervised models
Self-supervision has emerged as an effective paradigm for learning representations of
spoken language from raw audio without explicit labels or transcriptions. Self-supervised …
spoken language from raw audio without explicit labels or transcriptions. Self-supervised …
Efficiency-oriented approaches for self-supervised speech representation learning
Self-supervised learning enables the training of large neural models without the need for
large, labeled datasets. It has been generating breakthroughs in several fields, including …
large, labeled datasets. It has been generating breakthroughs in several fields, including …