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

本文提出了一种基于视觉转换器的图像编码器和知识蒸馏技术来压缩模型参数，并通过在低资源设备上从微小的显示内容片段生成音频的高效端到端神经网络结构，实现了可用于减轻视觉障碍的图像转语音系统的部署。人工和自动评估结果表明，我们的方法在性能方面几乎没有下降，并且可以加快推理时间 22%。