Self-supervised learning for automatic speech recognition In low-resource environmentsTools Fatehi, Kavan (2024) Self-supervised learning for automatic speech recognition In low-resource environments. PhD thesis, University of Nottingham.
AbstractSupervised deep neural networks trained with substantial amounts of annotated speech data have demonstrated impressive performance across a spectrum of spoken language processing applications, frequently establishing themselves as the leading models in respective competitions. Nonetheless, a significant challenge arises from the heavy reliance on extensive annotated data for training these systems. This reliance poses a significant scalability limitation, hindering the continual enhancement of state-of-the-art performance. Moreover, it presents a more fundamental obstacle for deploying deep neural networks in speech-related domains where acquiring labeled data is inherently arduous, expensive, or time-intensive, which are considered as low-resource ASR problems in this thesis.
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