Training dynamic models using early exits for automatic speech recognition on resource-constrained devices

arXiv:2309.09546v2 Announce Type: replace-cross
Abstract: The ability to dynamically adjust the computational load of neural models during inference is crucial for on-device processing scenarios characterised by limited and time-varying computational resources. A promising solution is presented by early-exit architectures, in which additional exit branches are appended to intermediate layers of the encoder. In self-attention models for automatic speech recognition (ASR), early-exit architectures enable the development of dynamic models capable of adapting their size and architecture to varying levels of computational …

abstract architectures arxiv automatic speech recognition computational cs.cl cs.sd devices dynamic eess.as exit exits inference processing recognition resources solution speech speech recognition training type