  This paper describes an end-to-end (E2E) neural architecture for the audio
rendering of small portions of display content on low resource personal
computing devices. It is intended to address the problem of accessibility for
vision-impaired or vision-distracted users at the hardware level. Neural
image-to-text (ITT) and text-to-speech (TTS) approaches are reviewed and a new
technique is introduced to efficiently integrate them in a way that is both
efficient and back-propagate-able, leading to a non-autoregressive E2E
image-to-speech (ITS) neural network that is efficient and trainable.
Experimental results are presented showing that, compared with the non-E2E
approach, the proposed E2E system is 29% faster and uses 19% fewer parameters
with a 2% reduction in phone accuracy. A future direction to address accuracy
is presented.
