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People with visual impairments have difficulty accessing touchscreen-enabledpersonal computing devices like mobile phones and laptops. The image-to-speech(ITS) systems can assist them in mitigating this problem, but their huge modelsize makes it extremely hard to be deployed on low-resourced embedded devices.In this paper, we aim to overcome this challenge by developing an efficientendto-end neural architecture for generating audio from tiny segments ofdisplay content on low-resource devices. We introduced a visiontransformers-based image encoder and utilized knowledge distillation tocompress the model from 6.1 million to 2.46 million parameters. Human andautomatic evaluation results show that our approach leads to a very minimaldrop in performance and can speed up the inference time by 22%.

Compression of end-to-end non-autoregressive image-to-speech system for low-resourced devices