Self-Supervised Speech Representation Learning: A Review. (arXiv:2205.10643v2 [cs.CL] UPDATED)

Although supervised deep learning has revolutionized speech and audio
processing, it has necessitated the building of specialist models for
individual tasks and application scenarios. It is likewise difficult to apply
this to dialects and languages for which only limited labeled data is
available. Self-supervised representation learning methods promise a single
universal model that would benefit a wide variety of tasks and domains. Such
methods have shown success in natural language processing and computer vision
domains, achieving new levels of performance …

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