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- W4386875779 abstract "The possibility of dynamically modifying the computational load of neural models at inference time is crucial for on-device processing, where computational power is limited and time-varying. Established approaches for neural model compression exist, but they provide architecturally static models. In this paper, we investigate the use of early-exit architectures, that rely on intermediate exit branches, applied to large-vocabulary speech recognition. This allows for the development of dynamic models that adjust their computational cost to the available resources and recognition performance. Unlike previous works, besides using pre-trained backbones we also train the model from scratch with an early-exit architecture. Experiments on public datasets show that early-exit architectures from scratch not only preserve performance levels when using fewer encoder layers, but also improve task accuracy as compared to using single-exit models or using pre-trained models. Additionally, we investigate an exit selection strategy based on posterior probabilities as an alternative to frame-based entropy." @default.
- W4386875779 created "2023-09-20" @default.
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- W4386875779 date "2023-09-18" @default.
- W4386875779 modified "2023-10-01" @default.
- W4386875779 title "Training dynamic models using early exits for automatic speech recognition on resource-constrained devices" @default.
- W4386875779 doi "https://doi.org/10.48550/arxiv.2309.09546" @default.
- W4386875779 hasPublicationYear "2023" @default.
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